Image processing method and image converting apparatus

ABSTRACT

There is described an image processing method and an image converting apparatus for performing the image processing for recording the medical image information. The apparatus includes an image converting section to convert the image in respect to the original image signals; an image feature extracting section to extract image features for every pixel included in the plurality of pixels in respect to the original image signals; a sharpness information obtaining section to obtain sharpness information from the image converting section; a sharpness calculating section to calculate correlation between the image features and the sharpness information; and a frequency processing section to compensate for a sharpness level of the image, based on the correlation calculated by the sharpness calculating section, so as to reduce a variation of the sharpness level before and after the image converting section converts the image in respect to the original image signals.

BACKGROUND OF THE INVENTION

[0001] The present invention relates to an image processing forrecording or displaying image on a recording medium based on the imageinformation and, more exactly, relates to an image processing method andan image converting apparatus as well for performing the imageprocessing for recording with ink-jet the medical image informationobtained through an inputting device, such as medical photographingdevice.

[0002] These years, there have been invented methods of obtainingmedical radiographic information without using a radiographic film madeof silver-salt photosensitive material. For example, computedradiography (CR) has become popular; it is a device for readingradiographic image, in which a radiographic image, once stored on animaging plate comprising mostly of stimulable phosphor, is taken out asan stimulable phosphor light, using an excitation light, and then thelight is photoelectrically transferred into an image signal.

[0003] Of late, there has been proposed a device called flat paneldetector (FPD) that reads out radiographic information using radiationphosphor or radiation photoconductive substance combined with atwo-dimensional semiconductor detector such as a TFT switching device.

[0004] Besides, other radiographic image inputting devices better thansimple radiographic device, for example, a X-ray computer tomographicunit (X-ray CT unit) and a magnetic resonance image generator (MRIgenerator) have also come into wider use. Most of these medical imageinputting devices provide the image information in the form of digitalsignals.

[0005] A method most frequently employed for diagnosing these medicalimages is to record the image information on a transparent-typerecording medium and/or a reflex-type recording medium and observe theimage in the form of a hard copy. A recording type most frequentlyapplied to a medical image recorder that records the medical imageinformation on a recording medium is to record image on atransparent-type recording medium, made of silver-salt recordingmaterial, by laser exposure. With this recording type, monochromemulti-gradation image can be depicted with excellent gradient andbesides, recording the image on a transmission medium and observing itwith a transmission light enables to achieve high diagnostic resolution.

[0006] Besides, very recently, hopes are laid on a possibility ofrecording medical image using an ink-jet type recorder.

[0007] Though it is desirous for the above-mentioned medical imagerecorder to depict the image information obtained through a radiographicimage inputting device as truly as possible, blurredness unique to eachrecorder is caused-to the image in practice.

[0008] For example, with a type that records image by laser exposure,some blurredness results from a fact that the laser beam has a certainsize of diameter. With a so-called thermal recording type in which heatis added per every pixel to record image on a recording medium,blurredness results from a fact that the thermal head has a finite sizeor from the spread of heat. Even with an ink-jet recording type,blurredness results from various sizes of ink dots generated on arecording medium or from a fact that the coloring material of the inkblots and spreads into or on the recording medium.

[0009] Besides, when using a display monitor such as CRT or liquidcrystal display, blurredness of image is caused. The blurredness likethe above is likely to result in poor diagnostic resolution and wrongdiagnosis.

[0010] The above-mentioned blurredness of image differs in its level ondifferent medical image recorders and, besides, even on the same medicalimage recorder, the level of blurredness may vary with the density ofimage to be recorded. Thus, diagnosis with stable image quality isprevented.

[0011] For a laser exposure recording type, for example, reducing thelaser beam diameter will be useful to minimize the blurredness of image.This, however, results in a problem that very expensive optical systemis needed or that the reduced beam diameter causes recording unevennessand hence the image quality is rather deteriorated. For an ink-jetrecording type, reducing the emitted ink particle size will be useful toreduce the ink dot diameter. This, however, also results in a problemthat reducing the ink particle size is technically very difficult andthat the reduced ink particle size leads to lower recording speed.

[0012] Besides, when recording image by the ink-jet recording type, theink adhesion onto a recording medium may vary with the image density tobe recorded. The ease of movement of the color in the recording mediumdepends upon the ink adhesion onto the recording medium and therefore,the extent of spread of the ink, i.e. the sharpness characteristicvaries. As a result, there arises a problem that the sharpness varieswith the image density to be recorded and hence stable image qualitycannot be attained.

[0013] In addition, it may be preferable, to some extent, for acommercial ink-jet printer or similar device that an image afterexcessive correction of the sharpness may have a different conditionfrom that of the original image (for example, a condition where thesharpness is higher than in the original image). In an application ofdiagnosing medical image, however, this leads to an inconvenience.

SUMMARY OF THE INVENTION

[0014] To overcome the abovementioned drawbacks in conventionalimage-processing methods and image-converting apparatus, it is an objectof the present invention to provide image-processing methods andimage-converting apparatus, which makes it possible to produce images instable quality, irrespective of density differences of the images to berecorded or differences in the conversion characteristics of variouskinds of apparatus.

[0015] Further, it is a feature of the present invention that thefrequency processing for correcting the sharpness level of image isperformed, based on the image features or sharpness information, so thatthe variation between the sharpness levels before and after the imageconversion step of the original image signal becomes smaller. Herein,“correcting the sharpness level of image so that the variation betweenthe sharpness levels before and after the image conversion step of theoriginal image signal” means that, when each of the sharpness levelcharacteristic of the image information (image signal or image itself)obtained by directly executing the image conversion step for theoriginal image signal and the sharpness level characteristic of theimage (image signal or image itself) obtained by executing the imageconversion step for the original image signal after performing thefrequency processing beforehand is compared to the sharpness levelcharacteristic peculiar to the original image signal, the latter, thatis, the sharpness level characteristic of the image (image signal orimage itself) subjected to the frequency processing is closer to that ofthe original image signal than the former. “Correcting the sharpnesslevel” means not only to increase the sharpness level that has decreaseddue to deterioration but also to return the sharpness level that hasincreased from the original condition to the original.

[0016] Accordingly, to overcome the cited shortcomings, theabovementioned object of the present invention can be attained byimage-processing methods and apparatus described as follow.

[0017] (1) A method for processing original image signals of an imagecomposed of a plurality of pixels, the method comprising the steps of:converting the image in respect to the original image signals;extracting image features for every pixel included in the plurality ofpixels in respect to the original image signals; and performing afrequency processing operation for compensating for a sharpness level ofthe image, based on the image features extracted in the extracting step,so as to reduce a variation of the sharpness level before and after theconverting step.

[0018] (2) The method of item 1, wherein the converting step isperformed after applying an image interpolation processing to theoriginal image signals; and wherein, when an interpolating-magnificationfactor of the image interpolation processing is equal to or greater thana predetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0019] (3) The method of item 1, wherein the image is a medical image.

[0020] (4) A method for processing original image signals of an imagecomposed of a plurality of pixels, the method comprising the steps of:converting the image in respect to the original image signals; obtainingsharpness information from an image converting apparatus by which theconverting step is performed; and performing a frequency processingoperation for compensating for a sharpness level of the image, based onthe sharpness information obtained in the obtaining step, so as toreduce a variation of the sharpness level before and after theconverting step.

[0021] (5) The method of item 4, wherein the converting step isperformed after applying an image interpolation processing to theoriginal image signals; and wherein, when an interpolating-magnificationfactor of the image interpolation processing is equal to or greater thana predetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0022] (6) The method of item 4, wherein the image is a medical image.

[0023] (7) The method of item 4, wherein the sharpness informationrelate to at least one of SWTF, MTF and ARTF.

[0024] (8) A method for processing original image signals of an imagecomposed of a plurality of pixels, the method comprising the steps of:converting the image in respect to the original image signals;extracting image features for every pixel included in the plurality ofpixels in respect to the original image signals; obtaining sharpnessinformation from an image converting apparatus by which the convertingstep is performed; calculating correlation between the image featuresand the sharpness information; and performing a frequency processingoperation for compensating for a sharpness level of the image, based onthe correlation calculated in the calculating step, so as to reduce avariation of the sharpness level before and after the converting step.

[0025] (9) The method of item 8, wherein the converting step isperformed after applying an image interpolation processing to theoriginal image signals; and wherein, when an interpolating-magnificationfactor of the image interpolation processing is equal to or greater thana predetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0026] (10) The method of item 8, wherein the image is a medical image.

[0027] (11) The method of item 8, wherein a neural network is employedfor the calculating step.

[0028] (12) The method of item 8, wherein the frequency processingoperation is performed, based on a formula of,

Snew=ΣCmSm

[0029] where

[0030] m=0, . . . , M. M: natural number,

[0031] Snew: processed image signal;

[0032] Sm: original image signal (when m=0) or unsharp image signalgenerated through a m-th unsharp masking processing (when m=1 to M),

[0033] Cm: weight factor in the original image signal (when m 0) orweight factor in the m-th unsharpe image signal (when m=1 to M)).

[0034] (13) The method of item 12, wherein the weight factor isdetermined, based on the image features or the sharpness information.

[0035] (14) The method of item 12, wherein the weight factor isdetermined so as to keep ΣCm at a constant value.

[0036] (15) The method of item 12, wherein the frequency processing isapplied to image signals generated from a mammographic image byemploying at least three unsharp masks, modulation transfer functions ofwhich are different each other.

[0037] (16) A method for processing an image composed of a plurality ofpixels, the method comprising the steps of: applying an unsharp maskprocessing to original image signals of the image, composed of theplurality of pixels, to create a plurality of unsharp image signals;integrating at least two of first differential image signals between theoriginal image signals and the plurality of unsharp image signals,second differential image signals between the unsharp image signalsbeing different relative to each other, third differential image signalsbetween the original image signals and image signals obtained byintegrating the first differential image signals, fourth differentialimage signals between the original image signals and image signalsobtained by integrating the second differential image signals, fifthdifferential image signals between lowest-frequency image signals forthe original image signals and image signals obtained by integrating thefirst differential image signals, and sixth differential image signalsbetween the lowest-frequency image signals for the original imagesignals and image signals obtained by integrating the seconddifferential image signals, in order to generate a compensation signal;adding the compensation signal to the original image signals or thelowest-frequency image signals for the original image signals togenerate processed image signals; and performing a frequency processingoperation for compensating for a sharpness level of the image, bychanging a modulation transfer function with respect to the unsharp maskprocessing, so as to reduce a variation of the sharpness level beforeand after a step of converting the image in respect to the originalimage signals.

[0038] (17) The method of item 16, wherein the converting step isperformed after applying an image interpolation processing to theoriginal image signals; and wherein, when an interpolating-magnificationfactor of the image interpolation processing is equal to or greater thana predetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0039] (18) The method of item 16, wherein the image is a medical image.

[0040] (19) The method of item 16, wherein the frequency processing isapplied to image signals generated from a mammographic image byemploying at least three unsharp masks, modulation transfer functions ofwhich are different each other.

[0041] (20) The method of item 16, wherein the unsharp mask processingis repeated processing with a specific mask.

[0042] (21) The method of item 20, wherein the specific mask is a simplemean mask.

[0043] (22) The method of item 20, wherein the specific mask is a simplemean mask of 2-pixels by 2-pixels.

[0044] (23) The method of item 1, wherein the frequency processingoperation for compensating for the sharpness level of the image isperformed so that frequency characteristics before and after theconverting step coincides each other in a predetermined frequency range.

[0045] (24) The method of item 23, wherein the predetermined frequencyrange is 0-3.0 cycle/mm.

[0046] (25) An apparatus for processing original image signals of animage composed of a plurality of pixels, the apparatus comprising: animage converting section to convert the image in respect to the originalimage signals; an image feature extracting section to extract imagefeatures for every pixel included in the plurality of pixels in respectto the original image signals; and a frequency processing section tocompensate for a sharpness level of the image, based on the imagefeatures extracted by image feature extracting section, so as to reducea variation of the sharpness level before and after the image convertingsection converts the image in respect to the original image signals.

[0047] (26) The apparatus of item 25, wherein the image convertingsection converts the image in respect to the original image signalsafter applying an image interpolation processing to the original imagesignals; and wherein, when an interpolating-magnification factor of theimage interpolation processing is equal to or greater than apredetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0048] (27) The apparatus of item 25, wherein the image is a medicalimage.

[0049] (28) An apparatus for processing original image signals of animage composed of a plurality of pixels, the apparatus comprising: animage converting section to convert the image in respect to the originalimage signals; a sharpness information obtaining section to obtainsharpness information from the image converting section; and a frequencyprocessing section to compensate for a sharpness level of the image,based on the sharpness information obtained by the sharpness informationobtaining section, so as to reduce a variation of the sharpness levelbefore and after the image converting section converts the image inrespect to the original image signals.

[0050] (29) The apparatus of item 28, wherein the image convertingsection converts the image in respect to the original image signalsafter applying an image interpolation processing to the original imagesignals; and wherein, when an interpolating-magnification factor of theimage interpolation processing is equal to or greater than apredetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0051] (30) The apparatus of item 28, wherein the image is a medicalimage.

[0052] (31) The apparatus of item 28, wherein the sharpness informationrelate to at least one of SWTF, MTF and ARTF.

[0053] (32) An apparatus for processing original image signals of animage composed of a plurality of pixels, the apparatus comprising: animage converting section to convert the image in respect to the originalimage signals; an image feature extracting section to extract imagefeatures for every pixel included in the plurality of pixels in respectto the original image signals; a sharpness information obtaining sectionto obtain sharpness information from the image converting section; asharpness calculating section to calculate the correlation between theimage features and the sharpness information; and a frequency processingsection to compensate for a sharpness level of the image, based on thecorrelation calculated by the sharpness calculating section, so as toreduce a variation of the sharpness level before and after the imageconverting section converts the image in respect to the original imagesignals.

[0054] (33) The apparatus of item 32, wherein the image convertingsection converts the image in respect to the original image signalsafter applying an image interpolation processing to the original imagesignals; and wherein, when an interpolating-magnification factor of theimage interpolation processing is equal to or greater than apredetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0055] (34) The apparatus of item 32, wherein the image is a medicalimage.

[0056] (35) The apparatus of item 32, wherein a neural network isemployed for the sharpness calculating section.

[0057] (36) The apparatus of item 32, wherein the frequency processingsection compensates for the sharpness level of the image, based on aformula of,

Snew=ΣCmSm

[0058] where

[0059] m=0, . . . , M. M: natural number,

[0060] Snew: processed image signal;

[0061] Sm: original image signal (when m=0) or unsharp image signalgenerated through a m-th unsharp masking processing (when m=1 to M),

[0062] Cm: weight factor in the original image signal (when m=0) orweight factor in the m-th unsharpe image signal (when m=1 to M)).

[0063] (37) The apparatus of item 36, wherein the weight factor isdetermined, based on the image features or the sharpness information.

[0064] (38) The apparatus of item 36,

[0065] wherein the weight factor is determined so as to keep ΣCm at aconstant value.

[0066] (39) The apparatus of item 36, wherein the frequency processingsection applies frequency processing to image signals generated from amammographic image by employing at least three unsharp masks, modulationtransfer functions of which are different each other.

[0067] (40) A apparatus for processing an image composed of a pluralityof pixels, the apparatus comprising: an image converting section toconvert the image in respect to the original image signals; an unsharpmask processing section to apply an unsharp mask processing to originalimage signals of the image, composed of the plurality of pixels, tocreate a plurality of unsharp image signals; an integrating section tointegrate at least two of first differential image signals between theoriginal image signals and the plurality of unsharp image signals,second differential image signals between the unsharp image signalsbeing different relative to each other, third differential image signalsbetween the original image signals and image signals obtained byintegrating the first differential image signals, fourth differentialimage signals between the original image signals and image signalsobtained by integrating the second differential image signals, fifthdifferential image signals between lowest-frequency image signals forthe original image signals and image signals obtained by integrating thefirst differential image signals, and sixth differential image signalsbetween the lowest-frequency image signals for the original imagesignals and image signals obtained by integrating the seconddifferential image signals, in order to generate a compensation signal;an adding section to add the compensation signal to the original imagesignals or the lowest-frequency image signals for the original imagesignals to generate processed image signals; and a frequency processingsection to compensate for a sharpness level of the image, by changing amodulation transfer function with respect to the unsharp maskprocessing, so as to reduce a variation of the sharpness level beforeand after the image converting section converts the image in respect tothe original image signals.

[0068] (41) The apparatus of item 40, wherein the image convertingsection converts the image in respect to the original image signalsafter applying an image interpolation processing to the original imagesignals; and wherein, when an interpolating-magnification factor of theimage interpolation processing is equal to or greater than apredetermined value, the image interpolation processing is performedafter performing the frequency processing operation, while, when theinterpolating-magnification factor of the image interpolation processingis smaller than the predetermined value, the frequency processingoperation is performed after performing the image interpolationprocessing.

[0069] (42) The apparatus of item 40, wherein the image is a medicalimage.

[0070] (43) The apparatus of item 40, wherein the frequency processingsection applies frequency processing to image signals generated from amammographic image by employing at least three unsharp masks, modulationtransfer functions of which are different each other.

[0071] (44) The apparatus of item 40, wherein the unsharp maskprocessing is repeated processing with a specific mask.

[0072] (45) The apparatus of item 44, wherein the specific mask is asimple mean mask.

[0073] (46) The apparatus of item 44, wherein the specific mask is asimple mean mask of 2-pixels by 2-pixels.

[0074] (47) The apparatus of item 25, wherein the frequency processingsection compensates for the sharpness level of the image, so thatfrequency characteristics, before and after the image converting sectionconverts the image in respect to the original image signals, coincideseach other in a predetermined frequency range.

[0075] (48) The apparatus of item 47, wherein the predeterminedfrequency range is 0-3.0 cycle/mm.

[0076] Further, to overcome the abovementioned problems, other imageprocessing methods and apparatus, embodied in the present invention,will be described as follow:

[0077] (49) An image processing method characterized in that, in theimage processing method including an image conversion step of theoriginal image signal comprising multiple pixels, image feature isextracted from each of the multiple pixels of the original image signal,and frequency processing for correcting the sharpness level of the imageis performed, based on the image feature, so that the variation betweenthe sharpness levels before and after the image conversion step of theoriginal image signal becomes smaller.

[0078] According to the present invention on an image processing method,when the image processing method that includes an image conversion stepof the original image signal comprising multiple pixels is performed,image feature is extracted from each of the multiple pixels of theoriginal image signal, and frequency processing for correcting thesharpness level of the image is performed, based on the image feature,so that the variation between the sharpness levels before and after theimage conversion step of the original image signal becomes smaller.

[0079] That is to say, through the frequency processing for correctingthe variation of sharpness level, not only the sharpness level that hasdecreased due to deterioration is increased but also the sharpness levelthat has increased from the original condition is returned to theoriginal. As a result, images can be provided in stable qualityirrespective of the difference in the density of image to be recorded ordifference in the conversion characteristic of each device.

[0080] (50) An image processing method characterized in that, in theimage processing method including an image conversion step of theoriginal image signal comprising multiple pixels, sharpness informationis obtained from an image converting apparatus that executes the imageconversion step, and frequency processing for correcting the sharpnesslevel of the image is performed, based on the sharpness information, sothat the variation between the sharpness levels before and after theimage conversion step of the original image signal becomes smaller.

[0081] According to the present invention on an image processing method,when the image processing method that includes an image conversion stepof the original image signal comprising multiple pixels is performed,sharpness information is obtained from an image converting apparatusthat executes the image conversion step, and frequency processing forcorrecting the sharpness level of the image is performed, based on thesharpness information, so that the variation between the sharpnesslevels before and after the image conversion step of the original imagesignal becomes smaller.

[0082] That is to say, through the frequency processing for correctingthe variation of sharpness level, not only the sharpness level that hasdecreased due to deterioration is increased but also the sharpness levelthat has increased from the original condition is returned to theoriginal. As a result, images can be provided in stable qualityirrespective of the difference in the density of image to be recorded ordifference in the conversion characteristic of each device.

[0083] (51) An image processing method characterized in that, in theimage processing method including an image conversion step of theoriginal image signal comprising multiple pixels, image feature isextracted from each of the multiple pixels of the original image signal,sharpness information is obtained from an image converting apparatusthat executes the image conversion step, and a sharpness adaptivecalculation for comparing the sharpness characteristic information withthe sharpness information is performed, and then frequency processingfor correcting the sharpness level of the image is performed, based onthe result of the sharpness adaptive calculation, so that the variationbetween the sharpness levels before and after the image conversion stepof the original image signal becomes smaller.

[0084] According to the present invention on an image processing method,when the image processing method that includes an image conversion stepof the original image signal comprising multiple pixels is performed,image feature is extracted from each of the multiple pixels of theoriginal image signal, sharpness information is obtained from an imageconverting apparatus that executes the image conversion step, and asharpness adaptive calculation for comparing the sharpnesscharacteristic information with the sharpness information is performed,and then frequency processing for correcting the sharpness level of theimage is performed, based on the result of the sharpness adaptivecalculation, so that the variation between the sharpness levels beforeand after the image conversion step of the original image signal becomessmaller.

[0085] That is to say, through the frequency processing for correctingthe variation of sharpness level, not only the sharpness level that hasdecreased due to deterioration is increased but also the sharpness levelthat has increased from the original condition is returned to theoriginal. As a result, images can be provided in stable qualityirrespective of the difference in the density of image to be recorded ordifference in the conversion characteristic of each device.

[0086] (52) The image processing method as described in item (50) or(51), characterized in that the sharpness information is sharpnessinformation on any one of SWTF, MTF or ARTF.

[0087] According to the present invention on an image processing method,as the sharpness information described in item (50) or (51), sharpnessinformation on any one of SWTF, MTF or ARTF of an image convertedthrough the image conversion step of the image converting apparatus isused.

[0088] As a result of the above, the variation of sharpness levelgenerated in the image conversion step of the image converting apparatuscan be measured appropriately, and hence images can be provided instable quality irrespective of the difference in the density of image tobe recorded or difference in the conversion characteristic of eachdevice.

[0089] (53) The image processing method as described in item (51),wherein the sharpness adaptive calculation is a computation utilizing aneural network.

[0090] According to the present invention on an image processing method,as the sharpness adaptive calculation described in item (51), acomputation utilizing a neural network is employed. As a result, thesharpness characteristic information can be compared with the sharpnessinformation under an appropriate condition through the neural network,and hence images can be provided in more stable quality irrespective ofthe difference in the density of image to be recorded or difference inthe conversion characteristic of each device.

[0091] (54) The image processing method as described in any one of items(49) through (53), characterized in that the frequency processing forcorrecting the sharpness level of the image is performed, based on aformula Snew=ΣCmSm (where m=0, . . . , M; M: natural number; Snew:processed image signal; Sm: original image signal (when m=0) or unsharpimage signal generated through the m-th unsharp masking processing (whenm=1 to M); Cm: weight factor in the original image signal (when m=0) orweight factor in the m-th unsharpe image signal (when m=1 to M)), sothat the variation between the sharpness levels before and after theimage conversion step of the original image signal becomes smaller.

[0092] According to the present invention on an image processing methodas described in items (49) through (53), as a result of employingso-called multi-resolution frequency processing in the frequencyprocessing, images can be provided in more stable quality irrespectiveof the difference in the density of image to be recorded or differencein the conversion characteristic of each device.

[0093] (55) The image processing method as described in item (54),characterized in that the weight factor is determined based on the imagefeature or sharpness information.

[0094] As a result of determining the weight factor in item (54) basedon the image feature or sharpness information, so-calledmulti-resolution frequency processing is performed, and hence images canbe provided in more stable quality irrespective of the difference in thedensity of image to be recorded or difference in the conversioncharacteristic of each device.

[0095] (56) The present invention is an image processing method asdescribed in item (54) or (55), characterized in that the weight factoris determined so that ΣCm becomes constant.

[0096] As a result of determining the weight factor in item (54) or (55)so that ΣCm becomes constant, so-called multi-resolution frequencyprocessing is performed, and hence images can be provided in much morestable quality irrespective of the difference in the density of image tobe recorded or difference in the conversion characteristic of eachdevice.

[0097] (57) An image processing method characterized in that multipleunsharp image signals are generated through unsharp masking processingof the original image signal comprising multiple pixels; a correctionsignal is obtained by integrating two, at least, or more of thedifferential image signal between the original image signal and multipleunsharp image signals, differential image signal between differentunsharp image signals, or differential image signal between the originalimage signal or lowest frequency image signal corresponding to theoriginal image signal and the image signal obtained by integrating theabove differential image signals; a processed image signal is obtainedby adding the correction signal to the original image signal or thelowest frequency signal corresponding to the original image signal; andthen frequency processing for correcting the sharpness level of theimage is performed, while changing the modulation transfer function forthe unsharp masking processing, so that the variation between thesharpness levels before and after the image conversion step of theoriginal image signal becomes smaller.

[0098] With a construction as above, the frequency characteristic of theprocessed image signal can be adjusted more minutely by changing thefrequency characteristic of the mask.

[0099] (58) The image processing method as described in any one of items(54) through (57), characterized in that the unsharp masking is repeatedmasking processing with a specific mask.

[0100] As a result of performing repeated masking with a specific maskin the unsharp masking processing for generating the unsharp imagesignal as described in items (6) through (9), so-called multi-resolutionfrequency processing is performed based on multiple band pass signals,and hence images can be provided in more stable quality irrespective ofthe difference in the density of image to be recorded or difference inthe conversion characteristic of each device.

[0101] (59) The image processing method as described in item (58),characterized in that the mask is a simple mean mask.

[0102] As a result of performing repeated processing with a specificfilter of a simple mean mask in the unsharp masking processing forgenerating the unsharp image signal as described in item (58), so-calledmulti-resolution frequency processing is performed based on multipleband pass signals, and hence images can be provided in more stablequality irrespective of the difference in the density of image to berecorded or difference in the conversion characteristic of each device.

[0103] (60) The present invention is an image processing method asdescribed in item (58) or (59), wherein the mask is a simple mean maskof 2-pixels by 2-pixels.

[0104] As a result of performing repeated processing with a specificfilter of a simple mean mask of 2-pixels by 2-pixels in the unsharpmasking processing for generating the unsharp image signal as describedin item (58) or (59), so-called multi-resolution frequency processing isperformed based on multiple band pass signals, and hence images can beprovided in more stable quality irrespective of the difference in thedensity of image to be recorded or difference in the conversioncharacteristic of each device.

[0105] (61) The image processing method as described in any one of items(49) through (60), characterized in that frequency processing forcorrecting the sharpness level of the image is performed, based on theresult of the sharpness adaptive calculation, in a specified frequencyband so that the frequency characteristics before and after the imageconversion step of the original image signal become approximately equal.

[0106] According to the present invention on an image processing methodas described in items (49) through (60), frequency processing forcorrecting the sharpness level of the image is performed in a specifiedfrequency band so that the frequency characteristics before and afterthe image conversion step become approximately equal.

[0107] As a result of the above, within a frequency band specified asdesired, images can be provided in stable quality irrespective of thedifference in the density of image to be recorded or difference in theconversion characteristic of each device.

[0108] (62) The image processing method as described in item (61),characterized in that the specified frequency band is 0 to 3.0 cycle/mm.

[0109] As a result of performing the frequency processing in a specifiedfrequency band of 0 to 3.0 cycle/mm, images can be provided in stablequality irrespective of the difference in the density of image to berecorded or difference in the conversion characteristic of each device.

[0110] (63) The present invention is an image processing method asdescribed in any one of items (54) through (62), wherein the number ofunsharp masks is three, at least, or more and the frequency processingis performed on the image signal generated from a mammographic image.

[0111] As a result that the number of unsharp masks is three, at least,or more and the frequency processing is performed on the image signalgenerated from a mammographic image, medical images for mammography canbe provided in more stable quality irrespective of the difference in thedensity of image to be recorded or difference in the conversioncharacteristic of each device.

[0112] (64) The image processing method as described in any one of items(49) through (63), characterized in that, when the image conversion stepis executed after image interpolation processing of the original imagesignal, the image interpolation processing is performed after thefrequency processing if the image interpolation processing at animage-interpolating magnification factor equal to or greater than aspecified value, but the frequency processing is performed after theimage interpolation processing if the image interpolation processing atan image-interpolating magnification factor less than a specified value.

[0113] According to the present invention on an image processing methodas described in items (49) through (63), the image interpolationprocessing is performed after the frequency processing if the imageinterpolation processing at an image-interpolating magnification factorequal to or greater than a specified value, but the frequency processingis performed after the image interpolation processing if the imageinterpolation processing at an image-interpolating magnification factorless than a specified value.

[0114] As a result of altering the turn of the image interpolationprocessing and the frequency processing according to theimage-interpolating magnification factor, deterioration of imagedecreases and images can be provided in more stable quality irrespectiveof the difference in the density of image to be recorded or differencein the conversion characteristic of each device.

[0115] (65) The image processing method as described in any one of items(49) through (64), characterized in that the frequency processing isperformed on the original image signal generated from a medical image.

[0116] As a result of performing the image processing as described initems (49) through (64), medical images can be provided in more stablequality irrespective of the difference in the density of image to berecorded or difference in the conversion characteristic of each device.

[0117] (66) An image converting apparatus characterized in that, theimage converting apparatus is provided with an image processing meansfor performing image processing by an image processing method asdescribed in any one of items (49) through (65).

[0118] According to the present invention on an image convertingapparatus, through the frequency processing for correcting the variationof sharpness level, not only the sharpness level that has decreased dueto deterioration is increased but also the sharpness level that hasincreased from the original condition is returned to the original.

[0119] As a result, in processing images, images can be provided instable quality irrespective of the difference in the conversioncharacteristic of each image processor.

[0120] (67) An image converting apparatus characterized in that, theimage converting apparatus is provided with an image processing meansfor performing image processing by an image processing method asdescribed in any one of items (49) through (65) and an image displayingmeans for displaying the image signal on which the image processing hasbeen performed by the image processing means.

[0121] According to the present invention on an image convertingapparatus, through the frequency processing for correcting the variationof sharpness level, not only the sharpness level that has decreased dueto deterioration is increased but also the sharpness level that hasincreased from the original condition is returned to the original.

[0122] As a result, in displaying images, images can be provided instable quality irrespective of the difference in the conversioncharacteristic of each image display.

[0123] (68) An image converting apparatus characterized in that, theimage converting apparatus is provided with an image processing meansfor performing image processing by an image processing method asdescribed in any one of items (49) through (65) and an image recordingmeans for recording on a recording medium the image signal on which theimage processing has been performed by the image processing means.

[0124] According to the present invention on an image convertingapparatus, through the frequency processing for correcting the variationof sharpness level, not only the sharpness level that has decreased dueto deterioration is increased but also the sharpness level that hasincreased from the original condition is returned to the original.

[0125] As a result, in recording images, images can be provided instable quality irrespective of the difference in the density of image tobe recorded or the difference in the conversion characteristic of eachimage recorder.

[0126] (69) The image converting apparatus as described in any one ofitems (66) to (68), characterized in that the image converting apparatusis provided with a sharpness information inputting means for inputtingthe sharpness information into this image converting apparatus.

[0127] According to the present invention on an image convertingapparatus, as a result of having a sharpness information inputting meansthat inputs the sharpness information, suitable sharpness informationcan be inputted and images can be provided in stable quality.

[0128] (70) The image converting apparatus as described in any one ofitems (66) to (69), characterized in that the image processing meansperforms the image processing on the original image signal generatedfrom a medical image.

[0129] Through the image processing on medical images as described initems (66) to (69), images can be provided in more stable qualityirrespective of the difference in the density of image to be recorded ordifference in the conversion characteristic of each device.

BRIEF DESCRIPTION OF THE DRAWINGS

[0130] Other objects and advantages of the present invention will becomeapparent upon reading the following detailed description and uponreference to the drawings in which:

[0131]FIG. 1 is a block diagram showing a brief electrical constructionof an image recorder according to an embodiment example of the presentinvention;

[0132]FIG. 2 is a block diagram showing a characterized portion of animage recorder according to an embodiment example of the presentinvention;

[0133]FIG. 3(a), FIG. 3(b), FIG. 3(c), FIG. 3(d) and FIG. 3(e) areexplanatory figures showing a correction process in an embodimentexample of the present invention;

[0134]FIG. 4(a) and FIG. 4(b) are explanatory figures showing acorrection process in an embodiment example of the present invention;

[0135]FIG. 5 is an explanatory figure showing a correction process in anembodiment example of the present invention;

[0136]FIG. 6(a), FIG. 6(b), FIG. 6(c) and FIG. 6(d) are explanatoryfigures on an embodiment example of the present invention;

[0137]FIG. 7 is an explanatory figure on an embodiment example of thepresent invention;

[0138]FIG. 8(a), FIG. 8(b) and FIG. 8(c) are explanatory figures on anembodiment example of the present invention;

[0139]FIG. 9(a), FIG. 9(b), FIG. 9(c) and FIG. 9(d) are explanatoryfigures on an embodiment example of the present invention;

[0140]FIG. 10(a), FIG. 10(b), FIG. 10(c) and FIG. 10(d) are explanatoryfigures on an embodiment example of the present invention;

[0141]FIG. 11 is an explanatory figure on an embodiment example of thepresent invention;

[0142]FIG. 12 is an explanatory figure on an embodiment example of thepresent invention;

[0143]FIG. 13(a) and FIG. 13(b) are explanatory figures on an embodimentexample of the present invention;

[0144]FIG. 14 is an explanatory figure on an embodiment example of thepresent invention;

[0145]FIG. 15 is an explanatory figure on an embodiment example of thepresent invention;

[0146]FIG. 16 is an explanatory figure on an embodiment example of thepresent invention;

[0147]FIG. 17 is an explanatory figure on an embodiment example of thepresent invention;

[0148]FIG. 18 is an explanatory figure on an embodiment example of thepresent invention;

[0149]FIG. 19 is an explanatory figure on an embodiment example of thepresent invention;

[0150]FIG. 20 is an explanatory figure on an embodiment example of thepresent invention; and

[0151]FIG. 21 is an explanatory figure on an embodiment example of thepresent invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0152] Referring to the drawings, a preferred embodiment of the presentinvention is explained hereunder. The present invention, however, is notlimited to the concrete examples of the embodiment described hereunder.

[0153] <Definition of Image Converting Apparatus>

[0154] An “image conversion step” in this embodiment means any one ofthe following:

[0155] (1) a step for outputting an image based on an image signal(wherein outputting means recording or displaying),

[0156] (2) a step for performing image processing based on the originalimage signal, or

[0157] (3) a step for photographing a subject and converting the shapecharacteristic of the image into an image.

[0158] Hereunder, an object that is converted through the imageconversion step is called an “input object”, and an object that isobtained as a result of the conversion through the image conversion stepis called an “output object”.

[0159] In case (1) above, the input object is image signal and theoutput object is hard copy. In case (2) above, the input object is imagesignal and the output object is image signal. In case (3) above, theinput object is subject (object) and the output object is image signal.

[0160] The image conversion step of the present embodiment is applicableto any one of the image inputting, image processing, image displayingand image recording steps provided that either input object or outputobject is image signal. Because the mode of the input/output objects isdifferent in each case, different sharpness information may be used ineach image converting apparatus.

[0161] <Overall Structure of Image Recorder>

[0162] Description hereunder is made, using a concrete example of imagerecorder as an example of a device for performing the image processingmethod of the present invention and also as an example of a device towhich the image converting apparatus of the present invention applies.

[0163]FIG. 1 is a block diagram showing the function block relating tothe image recording by the image recorder. The image recorder 100 of thepresent embodiment has a recording head unit 120 as a recording meansfor recording images by ink emission.

[0164] A control means 101 controls each portion of the image recorder100 of the present embodiment. Besides, the control means 101 also takescharge of control over the frequency processing for correcting thevariation of sharpness level of the recorded image caused by the imagerecorder, recording medium or ink used for recording, of which controlis one of the characteristics of the present embodiment.

[0165]110 is an image processing means, to which an image signal isinputted from an external medical photographing device or storagedevice, and performs necessary image processing. The processing meansalso performs frequency processing for correcting the sharpness level ofthe recorded image caused by the image recorder, recording medium or inkused for recording, of which image processing is one of thecharacteristics of the present embodiment. Herein, “correcting thesharpness level” means not only to increase the sharpness level that hasdecreased due to deterioration but also to return the sharpness levelthat has increased from the original condition to the original.

[0166] Besides, an image signal input from an external device may besent via a network of various types. The image signal processed by theimage processing means 110 is sent to the image control means 101.

[0167] The recording head unit 120 is equipped with four recording heads120 a to 120 d in series for black ink K1 to K4 of different density,respectively, and a recording head control signal is supplied from thecontrol means 101 to each of them. These recording heads 120 a to 120 dmay be integrated or installed separately. Generating an image usingfour different types of ink as above enables to obtain higher qualityand better multi-gradation as an image used for medical diagnosis orreference. To generate an image for medical use that is required to havemulti-gradation, it is preferable to use three to four kinds of ink ofdifferent density. In order to eliminate string-like irregularitypeculiar to the image recorder, emitting the ink evenly from therecording head onto the recording surface is necessary, and as a result,thicker ink receiving layer is needed as the ink absorption increases.If the ink receiving layer is made thicker, scratches are apt to becaused on the recording surface, and hence more careful handling of therecording medium is required.

[0168] The ink emission mechanism of the ink-jet head may be an ink-jettype that utilizes the piezo electric effect or utilizes a bubbleforming force generated at the time when the ink is heated momentarily.The number of nozzle holes suitable for an ink-jet type for medicalapplication is about 64 to 512. The traveling speed of ink particles ispreferably 2 to 20 m/s and the amount of ink particles per emitted dropis preferable 1 to 50 pico litter.

[0169]130 is a carriage roller that carries the recording medium in themain scan direction. 140 is a recording head carriage means that carriesthe recording head in the sub scan direction. Herein, the carriageroller 130 carries the recording medium 4 in the arrow A direction basedon the recording medium carriage signal. The head unit carriage means140 is installed to allow the recording head unit 120 to move in thedirection B perpendicular to the carriage direction of the recordingmedium 4.

[0170] The recording head carriage means 140 moves the recording headunit 120 in the arrow B direction according to the head carriage signal.Each of the recording heads 120 a to 120 d generates an image on therecording medium 4 based on the recording head control signal. To thecontrol means 101, an image signal is sent from the image processingmeans 110, and to the image processing means 110, an image signal isinputted from an external photographing device or storage device. Inputto the image processing means may be sent via a network.

[0171] <Overall Construction of Sharpness Correction by Image Recorder>

[0172]FIG. 2 is a block diagram showing the function block relating tothe image recording accompanied by sharpness correction by the imagerecorder.

[0173] The image recorder 100 of the present embodiment has an imagerecording means 121, comprising a recording head unit 120 (See FIG. 1)that records images by ink emission.

[0174] A control means 101 not only controls each portion of the imagerecorder 100 of the present embodiment but also takes charge of controlover the frequency processing for correcting the variation of sharpnesslevel of the recorded image caused by the image recorder, recordingmedium or ink used for recording, of which control is one of thecharacteristics of the present embodiment.

[0175]110 is an image processing means, to which an image signal isinputted from an external medical photographing device or storagedevice, and performs necessary image processing (frequency processingfor correcting the variation of sharpness level). The processing meansalso frequency processing for correcting the sharpness level of therecorded image caused by the image recorder, recording medium or inkused for recording, of which image processing is one of thecharacteristics of the present embodiment. Herein, “correcting thesharpness level” means not only to increase the sharpness level that hasdecreased due to deterioration but also to return the sharpness levelthat has increased from the original condition to the original. Besides,an image signal input from an external device may be sent via a networkof various types. The image signal processed by the image processingmeans 110 is sent to the image control means 101.

[0176] The image recording means 121, which records images in variousrecording ways, is constructed as shown in FIG. 1 in case of ink-jettype recording. 131 is a recording medium carrying means including acarriage roller for carrying the recording medium 4.

[0177]150 is an evaluation result retaining means into which the “resultof sharpness evaluation”, to be described later, is inputted andretained. The evaluation result is referred to in the frequencyprocessing by the image processing means 110.

[0178]200 is an image outputted from the image recorder 100, and a testpattern 210 or medical image 220 is the one.

[0179]300 is an image evaluating means that evaluates the variation ofsharpness level of the test pattern 210. 310 is the “sharpnessevaluation result {α}”, that is, evaluation result from the imageevaluating means 300. The sharpness evaluation result {α} is retained inthe evaluation result retaining means 150. The above-mentionedevaluation result of the sharpness correction means the sharpnessinformation mentioned in each claim.

[0180] If the image recorder 100 is so designed that the sharpnessinformation such as a result of the sharpness evaluation is inputted bya sharpness information inputting means and the learning phase iscompleted automatically based on the sharpness information, it becomespossible to perform desired sharpness level correction as soon as anyimage signal is transferred to the image recorder. The sharpnessinformation inputting means can be of various types, includingpushbutton, dip switch, entry keyboard, and touch panel on a display,but not limited thereto, and any type is applicable provided that itallows smooth input of the sharpness information. For example, it can beone mounted on an operation panel, including an LED display, installedon an ink-jet type image recorder, or it can be an externally connecteddisplay such as CRT display, LC display or organic EL display. It ispreferable to integrate the image displaying means and sharpnessinformation inputting means because set-up for inputting the sharpnessinformation becomes easier.

[0181] <Explanation on Recording Medium

[0182] A characteristic of the recording medium described in the presentembodiment is that, in practice, a monochrome image is depicted on itwith liquid ink. It is preferable that the medium is a sheet with anarea of practically 15×10 cm or more, four corners being cut round, madeof colorless or blue resin with a thickness of 75 to 250 μm at least,having at least one void-type ink absorption layer on one side.

[0183] If the thickness is less than 75 μm, the medium is hard to handlebecause of sagging down of the sheet. If the thickness is more than 250μm, on the contrary, fairly heavy weight is disadvantageous in bringinga pile of the sheets.

[0184] Since the conventional X-ray film is a transparent sheet withoutcolor or with blue color, it is preferable that the recording medium forthe present embodiment is a transparent sheet made of colorless or bluecolor resin, in order to prevent the users from having a sense ofincongruity.

[0185] Further, the recording medium described in the present embodimentis preferably of a type that at least one void-type ink absorption layeris provided on at least one side and the other side with no inkabsorption layer has a matted layer so as to assure the mechanicalcarriage performance of printer or to prevent multiple sheets of film,when piled up, from sticking to each other.

[0186] The recording medium described in the present embodiment can beproduced by increasing the percentage of voids of the ink absorptionlayer as much as possible and subjecting the surface to mat treatment togenerate unevenness.

[0187] Besides, white metallic oxide such as titanium oxide or leadoxide can be added to the ink absorption layer or a layer under it.

[0188] It is also possible to form a layer on one side of the backingopposite to the other side having the ink absorption layer and dispersethe metallic oxide such as titanium oxide or lead oxide over the layer,or to provide the ink absorption layer on both sides of the backing.

[0189] Materials applicable to transparent backing, serving as therecording mediums described in the present embodiment, are polyestertype such as polyethylene-terephthalate (PET), cellulose ester type suchas nitro cellulose and cellulose acetate, and besides, polysulfone,polyimide, and polycarbonate. The sheet recording medium shallpreferably be colored blue. This blue color is added to prevent the eyefrom being dazzled by excessive transmitted light through non-imageportion as explained above, and also to produce an effect of allowing ablack image to appear favorably. Accordingly, since at least one side ofthe sheet backing is provided with an ink absorption layer, the backingof the recording medium must be subjected to corona discharge treatment,flame treatment or ultraviolet ray irradiation treatment to enhance theadhesion of the ink absorption layer.

[0190] The ink absorption layer shall preferably be a layer ofthree-dimensional mesh structure having the percentage of voids of 40%to 90%. It is preferable that the three-dimensional mesh structure ismade of silica particles or organic particles, having an averageparticle size of 20 nm or less, and water-soluble resin, and the massratio of the silica particles or organic particles to the water-solubleresin is within a range of 1.2:1 to 12.1.

[0191] When the above is met, the pores that form the voids of thethree-dimensional mesh structure has an average diameter of 5 to 40 nmand the pores forming the voids has the pore capacity of 0.3 to 1 ml/g.

[0192] It is preferable that silica particles are of silicic acid,having two to three silarol groups per surface area 1 nm², and that thethree-dimensional mesh structure is made of chains that are formed bythe coupling of secondary particles, having a size of 10 to 100 nm, ofthe aggregated silica particles.

[0193] Applicable particles include, for example, colloidal silica,potassium silicate, zeolite, kaolinite, halloysite, muscovite, talc,calcium carbonate, calcium sulfate, and aluminum oxide.

[0194] Water-soluble resin shall preferably be polyvinyl alcohol, butgelatin or one disclosed in the Japanese Application Patent Laid-openPublication No. HEI 7-276789 (1995) is also applicable.

[0195] The ink absorption layer shall preferably have the specificsurface area of 50 to 500 m²/g. Besides, to prevent sheets, when piledup, from sticking to each other, it is preferable to disperse matparticles having an average particle size of 5 to 100 μm on the surface.

[0196] It is acceptable to add surface active agent as antistatic agent.

[0197] The surface with no ink absorption layer can be coated withgelatin or water-soluble resin to prevent curling. It is also acceptablethat this layer is subjected to antistatic treatment, mat treatment forpreventing sticking, and coloring blue, and also coated with metallicoxide particles such as titanium oxide particles and lead oxideparticles.

[0198] At the time of transmission radiograph observation, a number offilms are frequently treated. In order to ensure smooth recognition ofthe surface and rear of an image at a glance, it is preferable toprovide a notch, for example, on the upright corner of each sheet sothat the surface and rear of the sheet can easily be recognized.

[0199] <Description on Ink>

[0200] In the present embodiment, it is possible to generate an image byemitting multiple inks of different tone, using the ink-jet heads thatare a means for emitting multiple inks separately and independently.Besides, it is also possible to generate an image by emitting multiplemonochromatic inks of different density, using the ink-jet heads thatare a means for emitting multiple inks separately and independently.

[0201] In other words, when these inks are used independently or incombination as monochromic ink in multiple grades of density, forexample, two grades, three grades or four grades, different ink-jet headcan be employed for each ink density. For example, K1, K2, K3 and K4inks are applicable for generating a monochromatic image. For generatinga color image, each ink-jet head is needed for each ink, for example,yellow (Y), magenta (M), cyan (C), and black (B).

[0202] For the coloring material that solves or disperses into inkwater, any one of pigment, water-soluble dye and dispersing dye isapplicable.

[0203] Applicable pigment is any known organic or inorganic pigment. Forexample, inorganic pigment includes azo pigment such as azolake,insoluble azo pigment, condensed azo pigment, and chelate azo pigment,polycyclic pigment such as phthalocyanine pigment, perylene and perylenepigment, anthraxquinone pigment, quinaklydone pigment, dioxanezenepigment, thioindigo pigment, isoindolinone pigment, and quinophthalonipigment, dye lake such as basic dye type lake and acid dye type lake,and nitro pigment, nitroso pigment, aniline black, and daylightfluorescent pigment, and organic pigment includes carbon black.

[0204] Applicable equipment for dispersing the pigment includes ballmill, sand mill, At lighter, roll mill, agitator, Henschell mixer,colloid mill, ultrasonic homogenizer, purl mill, wet jet mill, and paintshaker. While dispersing the pigment, dispersing agent can also beadded. Applicable dispersing agent includes anion type or nonion typesurface active agent, and polymer dispersing agent.

[0205] The ink used in the present embodiment can be prepared as blackink by selecting suitable pigments or using a single kind of known dyeor dyes in combination.

[0206] Applicable water-soluble dye includes, for example, acid dye,basic dye, and reactive dye.

[0207] Applicable black dye includes, for example, CI (color index)Direct Black 9, 17, 19, 22, 32, 51, 56, 62, 69, 77, 80, 91, 94, 97, 108,112, 113, 114, 117, 118, 121, 122, 125, 132, 146, 154, 166, 168, 173,and 199.

[0208] The ink used in the present embodiment can be prepared as blackink by selecting suitable pigments or using a single kind of known dyeor dyes in combination.

[0209] For example, water-soluble black ink that is liquid in normaltemperature is prepared when carbon black is used as pigment andethylene glycol material and surface active agent or antiseptic agentare mixed.

[0210] In case of using dye, water-soluble black ink that is liquid innormal temperature is prepared when Direct Black 19, Direct Black 159,Surfer Black 1, Acid Black 2, or CI Food Black 2 is mixed into asolution containing ethylene glycol, glycerin, surface active agent, andantiseptic agent. An appropriate amount of Direct Black 19 (blue ink) ismixed to adjust the tone.

[0211] In generating an image, it is preferable to employ a combinationof inks with different density and tone, using the inks prepared asabove, vary the finely in harmony with the variation of density of theimage, and cover as wide density range as possible.

[0212] When inks of different tone are employed, Acid Blue 9, Acid Red52 or 94, Acid Yellow 23, Direct Yellow 86 or 142 is used as coloringmaterial. Besides, for example, use of an ink disclosed in the JapaneseApplication Patent Laid-open Publication No. 2000-129182 is alsopreferable in this embodiment.

[0213] Applicable water-soluble organic solvent includes alcohol group(for example, alcohols (for example, methanol, ethanol, isopropanol,butanol, isbbutanol, secondarybutanol, tertiarybutanol, pentanol,hexanol, cyclohexanol, and benzyl alcohol), polyatomic alcohol group(for example, ethylene glycol, diethylene glycol, triethylene glycol,polyethylene glycol, propylene glycol, dipropylene glycol, polypropyleneglycol, butylene glycol, hexanediol, pentanediol, glycerin, hexanetriol,and thiodiglycol) polyatomic alcohol ether group (for example, ethyleneglycol monomethyl ether, ethylene glycol monoethyl ether, ethyleneglycol monobutyl ether, diethylene glycol monomethyl ether, diethyleneglycol monoethyl ether, diethylene glycol monobutyl ether, propyleneglycol monomethyl ether, propylene glycol monobutyl ether, ethyleneglycol monomethyl ether acetate, triethylene glycol monomethyl ether,triethylene glycol monoethyl ether, triethylene glycol monobutyl ether,ethylene glycol monophenyl ether, and propylene glycol monophenylether), amine group (for example, ethanol amine, diethanol amine,triethanol amine, N-methyl diethanol amine, N-ethyl diethanol amine,morpholine, N-ethyl morpholine, ethylene diamine, diethylene diamine,triethylene tetramine, tetraethylene pentamine, polyethylene imine,pentamethyl diethylene triamine, and tetramethyl propylene diamine),amide group (for example, form amide, N,N-dimethyl form amido,N,N-dimethyl acetoamide), hetrocyclic group (for example, 2-pyrolidone,N-methyl-2-pyrolidone, cyclohexyl pyrolidone, 2-oxazolidone, and1,3-dimethyl-2-imida zolidinone), sulfoxid group (for example, dimethylsulfoxid), sulfone group (for example, sulfolane), urea, acetonitril,and acetone.

[0214] Surface active agent may be added to ink, as needed. Favorablesurface active agent for ink includes anionic surface active agent suchas dialkyl-sulfo succinic acid, alkyl naphthalene sulfonate, and fatacid salt, nonionic surface active agent such as polyoxy-ethylene alkylether, polyoxi-ethylene allyl ether, acetylene glycol, andpolyoxy-propylene block copolymer, and cationic surface active agentsuch as alkyl amine salt and Class-4 ammonium salt.

[0215] In addition to the above, other materials such as mildewpreventing agent, pH conditioning agent, and viscosity conditioningagent can be added to the ink, as needed.

[0216] <Operation of Image Recorder>

[0217] The image recorder of the present embodiment has an imageprocessing means 110 (See FIG. 1) for correcting the variation ofsharpness level because of the image recorder. So-called frequencyprocessing is applicable to the image processing in the image processingmeans 110. It is preferable that the characteristic of the inputtedimage signal is approximately equal to that of the recorded image as aresult of the correction through the frequency processing, because thefrequency characteristic of the inputted image data can be reproducedprecisely.

[0218] It is a characteristic of the present embodiment that imagefeature is extracted from each pixel of the original image signal,sharpness information is obtained from an image converting apparatusthat executes the image conversion step, and a sharpness adaptivecalculation for comparing the sharpness characteristic information withthe sharpness information is performed, and that frequency processingfor correcting the variation of sharpness level, caused in the imageconversion step of the original image, is performed based on the resultof the sharpness adaptive calculation.

[0219] That is to say, through the frequency processing for correctingthe variation of sharpness level, not only the sharpness level that hasdecreased due to deterioration is increased but also the sharpness levelthat has increased from the original condition is returned to theoriginal. As a result, images can be provided in stable qualityirrespective of the difference in the density of image to be recorded ordifference in the conversion characteristic of each device.

[0220] It is a characteristic of the present embodiment that theabove-mentioned sharpness information is sharpness information on anyone of SWTF, MTF or ARTF of an image converted through the imageconversion step of the image converting apparatus.

[0221] What are SWTF (rectangular wave response function) and MTF(modulation transfer function) is explained hereunder.

[0222] The test pattern image in FIG. 3(b), which is a test patter imagefor sharpness evaluation, is generated from the signal in FIG. 3(a). Thesharpness evaluation herein means obtaining the SWTF or MTF. In otherwords, the sharpness evaluation means obtaining the variation ofsharpness level caused in the image conversion step of the originalsignal.

[0223] The sharpness evaluation test pattern image (FIG. 3(b)) comprisesa normalized portion having the lowest spatial frequency and anon-normalized portion having other spatial frequencies. In thenormalized portion or non-normalized portion of the sharpness evaluationtest patter image, there is formed a mass of bars arranged at aspecified interval along the main scan direction (write direction) ofthe recorder. Hereinafter, a mass of the bars is called a chart element,and a mass of the chart elements is called a chart.

[0224] SWTF, which is synonymous with rectangular wave responsefunction, is generally calculated in the following steps. When an imagedata corresponding to a rectangular wave chart is generated, to startwith, and an image is recorded in the recorder based on the image data,a rectangular wave chart image is obtained. In a rectangular wave chartimage with N-number of chart elements, for example, if the chartelements are numbered in ascending order of spatial frequency, i=1 meansnormalized spatial frequency and i=2 to N means non-normalized spatialfrequency. Average part of the peak in the profile of the i-th chartelement (i=1 to N) and find the density DHi at the high level and thedensity DLi at the low level. Then, using the two values, obtain thecontrast Ci=(DHi−DLi)/(DHi+DLi) that represents the response of therecorder. Because the frequency is extremely low in the normalizedportion, it shall be adjusted so as not to cause deterioration of theimage sharpness. SWTF is now obtained as the contrast ratioSWTF(ui)=Ci/Cl by dividing the contrast of the non-normalized portion bythe contrast of the normalized portion. Here, ui means the spatialfrequency of the i-th chart element.

[0225] MTF, modulation transfer frequency, is equivalent to the spatialfrequency characteristic of a sine wave response.

[0226] How to calculate MTF using a rectangular wave chart is explainedbelow. Draw a smooth approximation curve SWTF(u) based on N-number ofplots of SWTF(ui) obtained by the above calculation, and convert it intoMTF(u) using the Coltmann's formula. This formula is described indetail, for example, in “Radiographic Image Information Technology (I)”(written by Uchida, Kanamori, and Inatsu; issued by the Japan RadiationTechnology Association) pages 167-172.

[0227] In reality, however, the test pattern in FIG. 3(b) is read out bythe image evaluation means 300 and a signal as shown in FIG. 3(c) isobtained. By referring to the highest SH and lowest SL of the signal,the MTF characteristic is obtained as shown in FIG. 3(d). Then,necessary correction level is calculated as shown in FIG. 3(e) so thatthe MTF characteristic becomes flat.

[0228] When calculating the MTF characteristic as above using arectangular wave chart, in order to make the sharpness levels before andafter the correction strictly identical, it is necessary to perform suchfrequency processing that acts upon the modulation transfer functionwhere MTF⁻¹(u)=1/MTF(u) holds true. Frequency processing applicable tothis includes a blurred masking processing or a method where an imagesignal is subjected to Fourier transformation, filtering processing inthe frequency space, and then inverse Fourier transformation. So-calledblurred masking (unsharp masking) processing is explained hereunder.

[0229] The sharpness level can be controlled through frequencyprocessing, where a blurred masking processing represented by thefollowing expression is employed. This control is disclosed in theJapanese Application Patent Laid-Open Publication Nos. Sho 55-163472,SHO 62-62373, and SHO 62-62376.

Sproc=Sorg+β×(Sorg−Sus)  (1)

[0230] where

[0231] Sproc: signal with exaggerated frequency, Sorg: original imagesignal, Sus: Blurred image signal, and β: exaggeration factor.

[0232] As image processing method has advanced these days, it becomespossible to achieve more sharp processing effect by a multi-resolutionmethod. Multi-resolution method is a way of obtaining a processed imagesignal, where an original image signal is decomposed into multiple imagesignals by frequency band and then a whole image is recovered afterspecified image processing is performed. Image processing using themulti-resolution method is introduced in “Digital Image Processing” (bySpringer-Verlag, 1991). For example, the Japanese Application PatentLaid-Open Publication No. Hei 10-75395 discloses preferred imageprocessing, using: $\begin{matrix}{{Sproc} = {{Sorg} + {{\beta ({Sorg})} \cdot {{Fusm}\left( {{Sorg},{Sus1},{Sus2},{\ldots \quad {SusN}}} \right)}}}} \\{{{Fusm}\left( {{Sorg},{Sus1},{Sus2},{\ldots \quad {SusN}}} \right)}} \\{= \left\{ {{{f1}\left( {{Sorg} - {Sus1}} \right)} + {{f2}\left( {{Sus1} - {Sus2}} \right)} + \ldots +} \right.} \\\left. {{{fk}\left( {{Susk} - 1 - {Susk}} \right)} + \ldots + {{fN}\left( {{SusN} - 1 - {SusN}} \right)}} \right\}\end{matrix}$

[0233] where

[0234] Sproc: signal with exaggerated high frequency component,

[0235] Sorg: original image signal,

[0236] Susk(k=1 to N): unsharp masking image signal,

[0237] fk(k=1 to N): factor for converting the each limited-band imagesignal, and

[0238] β(Sorg): exaggeration factor that is determined based on theoriginal image signal

[0239] The frequency processing methods as explained above areapplicable to the processing for correcting the sharpness level.

[0240]FIG. 4(a) and FIG. 4(b) show an example of the MTF characteristicin an ink-jet type image recorder. When employing, for example, an imageforming method where increased density is achieved by increasing thetotal volume of the emitted ink particles, the sharpness level decreasesin the periphery of a structure with high output density because the inkemission density becomes higher and hence ink spread can be causedeasily.

[0241] When it is relatively apparent that the sharpness level tends tovary as above, correction by a conventional method as disclosed, forexample, in FIG. 3 of the Japanese Application Patent Laid-OpenPublication No. Sho 55-163472 will be sufficient. If, however, thesharpness level varies depending upon the combination of density, theabove method can hardly correct the sharpness level in some cases.

[0242]FIG. 5 is a flowchart typically showing a brief correction processin the present embodiment. According to this embodiment, a test pattern210 is outputted beforehand from the image recorder 100, and then thetest pattern 210 is evaluated by the image evaluating means 300 and thesharpness information (evaluation result of the correction of thesharpness level) is obtained. Then, by comparing the test pattern 210with the image structure, the most appropriate correction of thesharpness level is performed.

[0243] Here, the “image structure” means, as a whole, qualitative orquantitative image characteristic of each pixel. For example, thequalitative characteristic includes characteristics that can be detectedvisually, such as a portion, shape and contour; and the quantitativecharacteristic includes characteristics that are expressed in quantity,such as an image signal value of a specific pixel and mean signal valueof peripheral pixels.

[0244] That is to say, in the present embodiment, correction of thesharpness is processed according to the flowchart in FIG. 5. In thechart, a corrected image is obtained as a result that the unsharpprocessing computation is executed (FIG. 5S1), the image feature of eachpixel of the original image signal is extracted (FIG. 5S2), a weightfactor for the image feature is calculated (FIG. 5S3), and a computationfor obtaining a corrected image signal is executed using the weightfactor (FIG. 5S5).

[0245] <Other Embodiment>

[0246] In the above-mentioned embodiment, description is made using aconcrete example of image recorder as an example of the image convertingapparatus. The image converting apparatus of the present embodiment isapplicable to any one of the image processing, image displaying andimage recording steps. Because the mode of the output object isdifferent in each case, different sharpness information may be used ineach image converting apparatus.

[0247] It is preferable to use a medical image, particularly a medicalimage of mammography as the image to be processed, because images can beprovided in stable quality irrespective of the difference in the densityof image to be recorded or difference in the conversion characteristicof each device.

[0248] Although the description in each embodiment as above uses anink-jet recording type as the recording means, the present invention isnot limited to use of the ink-jet recording type but any other recordingtypes, including wet or dry silver-salt laser recording type and thermaltransfer recording type, and any impact recording type such as wire dotrecording type, are applicable. In addition, there is no need to limitthe present invention to use of a serial recording type but so-calledline recording type is also applicable.

[0249] In addition, the image display to which the present invention canapply may be any one of CRT display, transmission or reflection typeliquid crystal display, organic EL display, plasma display, and thelike, without any limitation to the display type.

[0250] In addition, the type of the image inputting device(photographing device) includes radiographic unit such as CR and FPD,X-ray computer tomographic unit (X-ray CT unit), magnetic resonanceimage generator (MRI generator), ultra-sonic image diagnostic unit,electronic endoscopes, and retinal camera, but not limited thereto.

[0251] The present invention is very much efficient particularly in thefield of medical images that contain monochrome multi-gradation imagesignals and require to be of extremely high quality, because the effectof improved sharpness level is produced remarkably.

[0252] Besides, any embodiment, wherein software program code forrealizing the functions of the above-mentioned embodiments is suppliedto a computer in an apparatus or system connected with each of theabove-mentioned devices so as to operate the devices so that thefunctions of the embodiments are realized and then, the devices areoperated in accordance with a programs stored in the computer (CPU orMPU) of the system or apparatus, is included in the scope of the presentinvention.

[0253] Further, since the program code itself of the above softwarerealizes the functions of the embodiments in the above case, the programcode itself and also a means for supplying the program code into acomputer including, for example, a storage medium in which the programcode has been stored constitute the present invention. Applicablestorage medium includes, for example, flexible disk, hard disk, opticaldisk, opto-magnetic disk, CD-ROM, magnetic tape, non-volatile memorycard, and ROM.

[0254] Needless to say, not only in case that the functions of theabove-mentioned embodiments are realized as the computer executes thesupplied program code but also in case that the functions are realizedas the computer executes the program code in collaboration with an OS(operating system) or other application software operating on thecomputer, those program codes are included in the embodiment of thepresent invention.

[0255] Furthermore, when the supplied program code is once stored in thememory installed on a function expansion board of a computer or on anexpanded capability unit connected to a computer, and then a CPU or thelike mounted on the function expansion board or expanded capability unitperforms part or whole of actual processing in accordance with theinstructions of the program code and the functions of theabove-mentioned embodiments are realized as a result of the processing,the program code is, needless to say, included in the scope of thepresent invention.

[0256] [Example of Embodiment]

[0257] A concrete example of the embodiment of an image processingmethod that includes an image conversion step of the original imagesignal comprising multiple pixels, wherein image feature is extractedfrom each of the multiple pixels of the original image signal, and thesharpness level of the image is corrected, based on the image feature orsharpness information, so that the variation between the sharpnesslevels before and after the image conversion step of the original imagesignal becomes smaller, is explained hereunder, particularly using animage recorder as an example.

[0258] If the sharpness information of the image recorder can be knownin advance, it is possible to correct the sharpness level based on thesharpness information. If the sharpness characteristic of a test patternlike a rectangular wave chart is obtained in advance and suitablecorrection amount of the sharpness level is understood, it becomespossible to perform the most suitable sharpness level correction onto,at least, a rectangular wave test pattern.

[0259] The image to be recorded, however, is not always a test patternlike the rectangular wave chart, and appropriate sharpness levelcorrection must be performed for any type of image signal.

[0260] It, therefore, is necessary to extract the image feature from animage to be subjected to the sharpness level correction, check up theimage structure in the periphery of each pixel with the image feature,and perform appropriate sharpness correction based on the sharpnesscharacteristic analogized from the check-up.

[0261] The present embodiment can be briefly separated into two phases:a learning phase as pre-processing (See FIG. 18(a)) and a retrievingphase as actual sharpness correction (See FIG. 18(b)). Description belowis given separately on the learning phase and on the retrieving phase.

[0262] The pre-processing (learning phase) requires each of thepre-processing (1): sharpness evaluation of test pattern and definitionof sharpness information, pre-processing (2): determination of weightfactor suitable for the sharpness level correction, pre-processing (3):sharpness adaptive calculation (comparison of the sharpness informationwith the test pattern information), pre-processing (4): extraction ofthe image feature, and pre-processing (5): image structure check-upcomputation based on the image characteristic. Processing methods of theabove are explained hereunder in sequence. FIG. 18(a) shows the outlineof the processing in this learning phase.

[0263] <Pre-Processing (1): Sharpness Evaluation of Test Pattern andDefinition of Sharpness Information>

[0264] To start with, it is necessary to perform the sharpnessevaluation of the test pattern and define clearly the sharpnessinformation corresponding to the evaluation result. “Sharpnessinformation” means information either on the “sharpness level” thatrepresents the level of sharpness of an image or on the “sharpness” thatrepresents the characteristic. The former can be a physical evaluationvalue itself such as MTF and SWTF, and a factor used for approximatingthe sharpness level characteristic (MTF characteristic curve) with aspecified function type or a characteristic parameter related to thefactor are also applicable. The latter can be a quantified value of afunctional evaluation result from visual observation of an image,wherein functional evaluation method and quantification method do notmatter.

[0265] An example for obtaining the sharpness information based on theMTF sharpness level of a rectangular wave test pattern is explainedhereunder.

[0266]FIG. 18 shows a process for obtaining the sharpness information{α} 310 through the pre-processing (1).

[0267] Of the image signal representing the rectangular wave chart, thehigher signal value is denoted SH and the lower signal value is denotedSL (See FIG. 3(a)). The rectangular wave chart is recorded on arecording medium based on the image signal (See FIG. 3(b)), a densityprofile representing the rectangular wave chart is found using amicro-densitometer (See FIG. 3(c)), and then the sharpness MTF(up) isobtained as a result of mathematical computation and analysis of thedensity profile (See FIG. 3(d)). Herein, “up” is the spatial frequencyof the rectangular wave chart and, if the rectangular wave chartcomprises P-number of different spatial frequencies, p=1 to P applies.Then, MTF(u) is obtained in an approximating manner based on theanalysis result (up, MTF(up)). Applicable approximation function can beof various types, including exponential polynomial, Gauss function,Lorentz function, and the like, but not limited thereto. For example, anexpression MTF(α, u)=2/(1+exp(α|u|)) is also applicable.

[0268] Herein, α represents the sharpness information that is not only aparameter showing the extent of variation of the sharpness but also aparameter showing the extent of correction of the sharpness as describedlater (See FIG. 3(d)).

[0269] As MTF(0, u)=1 applies if α=1 and MTF(∞, u)=0 applies if α=in theabove expression, the sharpness turns to be better when α is smaller andworse when α is greater, and hence the frequency characteristic can beexpressed suitably and easily.

[0270] While, for simplification's sake, only one piece of sharpnessinformation (α) is used for one rectangular wave chart in the presentembodiment, it is not necessary to use only a piece of sharpnessinformation for a rectangular wave chart but multiple pieces ofinformation will do. Hereinafter, on an assumption that multiple piecesof the sharpness information may be available, the sharpness informationα is expressed as {α} in a generalized style.

[0271] Besides, it is recommended to prepare a combination {SHk, SLk} ofk-number of pieces each of the image signals SH and SL representing therectangular wave chart and find {αk} corresponding to them. Herein, SHk,SLk, and {αk} mean the higher signal value in the k-th rectangular wavechart, lower signal value of the same, and sharpness informationcorresponding to the k-th rectangular wave chart, respectively, and k=1to K holds true if k-number of rectangular wave charts are available. Inorder to improve the sharpness level correction accuracy, k is preferredto be sufficiently great. That is, it is preferable to prepare as manyrectangular wave charts as possible and find their sharpness informationin advance.

[0272] Besides, the sharpness information {α} can be an index valueitself such as MTF, SWTF or ARTF obtained through physical evaluation,or, when approximating the spatial frequency characteristic using aspecified function type, a parameter specific to the function type maybe applied. Various ways can be thought out, including, for example, acase where the MTF characteristic is regarded as a Gauss function, inwhich its value is 1 at the zero frequency and MTF attenuates as thespatial frequency increases, and the standard deviation obtained throughthe least square or the like is regarded the sharpness information {α}.Naturally, the approximation expression for MTF may be a polynomial orLorentz function, and the type of the function does not matter providedthat the MTF characteristic can be approximated precisely. Differentmethods other than the above can be applied also to the sharpnessadaptive calculation.

[0273] <Pre-Processing (2): Determination of Weight Factor Suitable forSharpness Level Correction>

[0274] Next, a weight factor suitable for the sharpness correction isdetermined. The “weight factor” means a weight factor in the originalimage signal and unsharp image signal, and the processed image signal isexhibited as a linear sum of the original image signal and unsharp imagesignal. When the weight factor in the m-th unsharp image signal isdenoted Cm, it is preferable that ΣCm is constant in order to maintainthe gradation balance of the whole image after the image processing andthat ΣCm=1 always holds true in order to maintain the gradationcharacteristic before and after the image processing.

[0275] With an image recorder having such sharpness level characteristicof which sharpness information is equal to the above-mentioned α, inorder to precisely recover the sharpness, taking the abovecharacteristic into account, it is sufficient to perform a frequencyprocessing equivalent to MTF⁻¹(α, u)=(1+exp(α|u|)/2 in advance, using aninverse MTF⁻¹(α, u) of the above.

[0276] The most conventional method for applying the frequencyprocessing being equivalent to MTF−1(α, u) is a method of employing atwo-dimensional mask processing. Although it is possible to employ theconvolution calculating operation for the two-dimensional maskprocessing mentioned above, it is also applicable to employ themultiple-resolution method in which the original image signal is dividedinto image signals having a plurality of (M number of) resolutioncharacteristics, and a new image is created by multiplying appropriateweighted coefficients Cm (m=0, 1, - - - , M) and adding them.Incidentally, the more the number of unsharp masks M is, the moreidealistic frequency processing can be achieved.

[0277]FIG. 6(a), FIG. 6(b), FIG. 6(c) and FIG. 6(d) are explanatoryfigures of a resolving operation using the two-dimensional maskprocessing, and concretely, show an example in which two-dimensionalmask of 7×7 is resolved into three unsharp sub-masks. Each of 81 numberof squares (9×9=81) shown in each of FIG. 6(a), FIG. 6(b), FIG. 6(c) andFIG. 6(d) indicates each of pixels two-dimensionally arranged, and theblack-colored square located at the center position is a target pixelfor which the unsharp image signals should be found. The peripheral areaof the black-colored square surrounded by the solid lines is equivalentto a pixel area being a calculating object when finding the unsharpimage signals in regard to the target pixel. While, the outside area,not surrounded by the solid lines, is equivalent to another pixel area,which is not a calculating object when finding the unsharp image signalsin regard to the target pixel.

[0278]FIG. 6(a), FIG. 6(b), FIG. 6(c) and FIG. 6(d) show the areas ofcalculating the unsharp image signals in regard to the original imagesignal (the sub-mask of 1×1), the sub-mask of 3×3, the sub-mask of 5×5,the sub-mask of 7×7, respectively. Initially, the unsharp image signalsof S0, S1, S2, S3 are generated by affecting the unsharp masks, sizes ofwhich are different relative to each other, to the original imagesignals representing the two-dimensional image. For instance, each ofthe unsharp image signals can be calculated by employing the unsharpmasks as the simple mean masks.

[0279] Incidentally, the term of “simple mean mask”, referred herein,represents a mask processing for finding an average value of all pixelsresiding in a calculating range of the unsharp image signals. Forinstance, in the sub-mask of M×N, all of the mask coefficients in regardto all pixels are constant value of 1/MN.

[0280] Successively, corrected image signals are found by applying theintegration-adding calculation using the unsharp image signals of S0,S1, S2, S3. When weight coefficients for the unsharp image signals ofS0, S1, S2, S3 are C0, C1, C2, C3, respectively, the image signals Sprocafter the image processing can be calculated by the equation ofSproc=ΣCmSm (m=0-3). As mentioned above, by performing the calculationafter resolving the two-dimensional mask into a plurality of unsharpsub-masks, it becomes possible to apply the two-dimensional maskprocessing to the original image signals while decreasing the memorycapacity and the number of calculation times. For instance, in case ofthe two-dimensional mask processing of 7×7, according to theconventional two-dimensional mask processing, it is necessary to storeall of weight coefficients, number of which is 7×7 49, and to retrieveeach of them one by one from the memory when performing the calculation.According to the present invention mentioned in the above, however, byperforming the calculation after resolving the two-dimensional mask intoa plurality of unsharp sub-masks, it becomes possible to perform thetwo-dimensional mask processing at a higher velocity than ever only bystoring the mask-size and weight coefficient Cm.

[0281]FIG. 7 shows an example of the weight coefficient setting inrespect to unsharp image signals. Concretely speaking, the optimumweight coefficients in the simple mean masks shown in FIG. 6(a), FIG.6(b), FIG. 6(c) and FIG. 6(d) are indicated, and the horizontal axis isthe sharpness information α, while the vertical axis is the weightcoefficients (C0-C3). As shown in the drawing, it is desirable that Cmcan be determined for one parameter as a simple correspondence. It isalso desirable, however, that Cm is a function for more than twoparameters.

[0282] If the sampling size equivalent to the actual size of each pixelin an image signal is Δs, the modulation transfer function of the simplemean filter is expressed by Fm(u)=sinc((2m+1)πuΔs), where sinc(x) meanssinc(x)=sin x/x. Therefore, it is the most preferable if MTF⁻¹(α,u)÷ΣCm(α)·Fm(u) holds true, and the most appropriate Cm(α) is obtainedusing an optimizing algorism such as the least square. The result isshown in FIG. 7. Herein, it is intended that ΣCm=1 holds true with anyα.

[0283] When an output pixel size equivalent to the smallest record size,of which output can be controlled in the image recorder, is denoted Δs′,the image-interpolating magnification factor δ is expressed as δ=Δs/Δs′.In order to perform suitable sharpness level correction in accordancewith the output size, image interpolation processing is first performedusing the image-interpolating magnification factor of δ, and then thesharpness information α is replaced with α/δ and the processing forcorrecting the sharpness level is performed. The image interpolationmethods known to us include simple interpolation (in case of imageinterpolation by integral multiplication), linear interpolation, andspline interpolation, but not limited thereto.

[0284] When the image interpolation processing is for enlargementinterpolation, that is, in case δ>1, number of image signal data afterthe image interpolation increases. It, therefore, is preferable toperform the sharpness level correction processing first, using thesharpness information α, and then perform the image interpolationprocessing, because number of computations in the image processingdecreases. On the other hand, when the image interpolation processing isfor reduction interpolation, that is, in case δ<1, number of imagesignal data after the image interpolation decreases. It, therefore, ispreferable to perform the image interpolation processing first, and thenreplace the sharpness information α with α/δ and perform the sharpnesslevel correction processing, because the number of computations in theimage processing decreases. Although the threshold of theimage-interpolating magnification factor that determines the processingturn of the image interpolation processing and sharpness levelcorrection processing is set to δ=1 herein, the threshold can be changedfreely according to the correction accuracy of the sharpness level andallowable image processing time.

[0285]FIG. 18(a) shows the pre-processing (2) performed in the imageprocessing means 110.

[0286] <Pre-Processing (3): Sharpness Compatibility>

[0287] Next, an example of the sharpness compatibility processing by asharpness comparing means is explained hereunder. The “sharpnesscompatibility” means to compare the information on the imagecharacteristic (to be described later) with the sharpness information ofthe output object obtained as a result of performing the imageconversion step based on the image signal representing theafore-mentioned test pattern. In the present embodiment, information onthe image feature means test pattern information on the image signalrepresenting the test pattern, but it can be the image feature itself.

[0288] “Test pattern information” means image signal representing a testpattern and, at the same time, the information must be on the testpattern before performing the image conversion step. Test patterninformation can be any combination of, for example, the highest signalvalue SH in the rectangular wave chart, lowest signal value SL,amplitude Save=(SH+SL)/2, and signal differential ΔS=SH−SL, because thechart profile is fixed by using any two characteristics of these. Testpattern information can also be the spatial frequency specific to therectangular wave chart.

[0289] “Information supply” in FIG. 18(a) means handing over the known“test pattern information” to the other.

[0290] For example, a method employing a neural network is suitable forcomparing the test pattern information {β} on the image signalrepresenting a test pattern like the rectangular wave chart with thesharpness information {α} on the test pattern. To be concrete, have thenetwork learn using the test pattern information and sharpnessinformation as a learning set (list of input/output pairs). When thetest pattern information {β} extracted from the test pattern is inputtedinto the network that has completed learning, the processing is completeas the sharpness information {α} is outputted.

[0291]FIG. 18(a) shows the pre-processing (3) wherein the test patterninformation {β} 311 is inputted and the sharpness information {α} 310 isoutputted.

[0292]FIG. 8(a) shows a brief diagram of the sharpness adaptivecalculation using a neural network. In the embodiment example, it is anetwork comprising two-layer perceptron, for which a learning methodbased on the error inverse-propagation is employed. Perceptron and errorinverse-propagation method are described in detail in “Neural Computer:Approach from Statistical Physics” (written by J. Hearts, A. Claw, R. G.Palmer; translated by Tatsuya Sasagawa, Isamu Kure; published by ToppanCo.; 1994, pages 109-139, 141-147).

[0293] The network comprises two input elements (Input1, Input2),intermediate layer with five elements, and one output element (Output).Each element can take a continuous value within a range of [0, 1]. Thenetwork, wherein the input elements are connected to each intermediateelement, and the intermediate elements to the output element, is sostructured as to transfer information from the input elements to theoutput element. When information is inputted in the network, individualcomputation is executed for each element depending upon the intensity ofthe synapse coupling between each element, and the output result isobtained through the intermediate layer. Adjusting the synapse couplingbetween each element by the error inverse-propagation method, continueletting the network learn until the output resulting from the input {β}becomes equal to {α}.

[0294] In this embodiment example, two pieces of test patterninformation SH/Smax and SL/Smax in the rectangular wave chart areinputted into the input elements. Herein, SH and SL mean the highestsignal value and lowest signal value in the rectangular wave chart,respectively, and Smax is the minimum image signal value, which, forexample, is Smax=4095 in an image with 12-bit graduation. The sharpnessinformation α/αmax corresponding to the result of the sharpness levelevaluation of the rectangular chart is outputted to the output element.Herein, αmax is the maximum of α, representing the possible extent ofthe sharpness level correction.

[0295] Learning for achieving the objective of the sharpness comparingmeans is explained hereunder. When the highest signal value, lowestsignal value and sharpness information of the k-th rectangular chart aredenoted SHk, SLk and αk, respectively, the objective is achieved whenSHk/Smax and SLk/Smax are inputted in terms of every k and α/αmax isoutputted.

[0296] If the intensity of the synapse coupling between each element atthe time of the completion of learning, and when a piece of informationon the rectangular wave chart is inputted to the network, an outputresult corresponding to the chart profile can be obtained. When this isrealized, an output result based on actual learning is retrieved in thenetwork and, even when any information on a rectangular wave chart aboutwhich the network has not learnt actually is inputted, an outputexhibiting the tendency of the chart can be obtained. For example, asshown in FIG. 4(a) and FIG. 4(b), we have actually confirmed that, incase of an image recorder in which the sharpness tends to decrease asthe image signal value increases, an output result exhibiting thetendency is obtained.

[0297] <Pre-Processing (4): Extraction of Image Feature>

[0298] Next, an example of extraction of the image feature is describedhereunder. FIG. 18(a) shows the pre-processing (4) for extracting theimage feature {γ} 312.

[0299] “Image feature” can be any quantity that characterizes the image.For example, a quantitative value calculated from the image signal valuecan be used, but not limited thereto, and any quantified value ofsubjective evaluation can be used instead. In addition, thecharacteristic quantity of an image needs not be limited to a functionof the original image or unsharp image but can be any characteristicquantity including a physical structure such as bone or organ of asubject, a shape being such as oblong or circular, and size of theshape. Extracting the image feature enables to find out the imagestructure in the periphery of each pixel in the image signal. The “imagestructure” means, as a whole, qualitative or quantitative imagecharacteristics of each pixel. For example, qualitative characteristicmeans the characteristic that can be detected visually, such as portion,shape, and contour, and quantitative characteristic means thecharacteristic that is represented by a value, such as local density,and existence of cyclic structure. In this embodiment example, fivecharacteristic quantities <1> S0/Smax, <2> S3/Smax, <3> |S0−S1|/Smax,<4> 51 S1−S2|/Smax, and <5> |S0−S2|/Smax are obtained from the originalimage signal S0 and three unsharp image signals S1, S2 and S3.

[0300] Herein, S0 is the original image and Sm means the processed imagemasked with the m-th unsharp mask (m=1, 2, 3). Taking the rectangularwave chart for example, <1> and <2> are equivalent to a local mean interms of the image structure and <3>, <4> and <5> are equivalent to theamplitude in terms of, the image structure.

[0301] In a computation for determining the weight factor of each pixel,wherein the image feature of the pixel in question is used, it isacceptable to use the image feature of an adjacent pixel instead of theabove. To be concrete, in obtaining the image feature of a pixel (i, j)in the two-dimensional image signal, it is acceptable to use a simplemean mask S2 (i−1, j+1) of the mask size of 5×5 for the pixel (i−1, j+1)for the computation, or it is also acceptable to use S2 (i−1, j+1) S2(i, j) as the image feature on the pixel (i, j).

[0302] <Pre-processing (5): Image Structure Check-Up Based on ImageFeature>

[0303] Next, an example of image structure check-up computation isexplained hereunder. The “image structure check-up” is to extract theimage feature of each pixel and its periphery in the original imagesignal before performing the sharpness level correction processing andcheck up the quantity with the test pattern information. In other words,it means to extract the image feature of each pixel and its periphery inthe original image signal and analogize a test pattern that mostresembles to the characteristic quantity in terms of the imagestructure. This means, for example, to extract the image feature of apixel and analogize that an adjacent pixel “has the image structure of arectangular wave equivalent to one with amplitude AS and spatialfrequency Uo”.

[0304] This pre-processing can be completed in a similar manner for thepre-processing (2), using the afore-mentioned neural network. Forexample, it is sufficient that the image feature {γ} of the originalimage signal before performing the sharpness level correction processingis compared with the afore-mentioned test pattern information {β}. FIG.18(a) shows the relationship between the image feature {γ} 312 in thepre-processing (3) and the test pattern information {β} 311.

[0305] To be concrete, have the network learn using the image feature,extracted from the test pattern image, and test pattern information as alearning set (list of input/output pairs). When the image feature {γ}extracted from the test pattern is inputted into the network that hascompleted learning, the processing is complete as the test patterninformation {β} is outputted.

[0306]FIG. 8(b) shows a brief diagram of the image structure check-upsystem using a neural network. In the embodiment example, it is anetwork comprising two-layer perceptron, for which a learning methodbased on the error inverse-propagation is employed.

[0307] The network comprises five input elements (Input1 to Input5),intermediate layer with five elements, and two output elements (Output1,Output2). Each element can take a continuous value within a range of [0,1]. The network, wherein the input elements are connected to eachintermediate element, and the intermediate elements to the outputelement, is so structured as to transfer information from the inputelements to the output element. When information is inputted in thenetwork, individual computation is executed for each element dependingupon the intensity of the synapse coupling between each element, and theoutput result is obtained through the intermediate layer. Adjusting thesynapse coupling between each element by the error inverse-propagationmethod, continue letting the network learn until the output resultingfrom the input {γ} becomes equal to {β}.

[0308] In this embodiment example, five pieces of quantitiesrepresenting the image feature in the rectangular wave chart, that is,SV1 to SV5, (SV1) S0/Smax, (SV2) S3/Smax, (SV3)|S(0)−S(1)|/Smax,(SV4)|S(1)−S(2)1/Smax, and (SV5)|S(0)−S(2)1/Smax are inputted.

[0309] Herein, SV1 to SV5 are the characteristic quantity in therectangular wave chart, respectively, and Smax is the maximum imagesignal value, which, for example, is Smax=4095 in an image with 12-bitgraduation. Two pieces of the test pattern information SH/Smax andSL/Smax in the rectangular wave chart are outputted to the outputelement.

[0310] Learning for achieving the objective of the sharpness comparingmeans is explained hereunder. When the five characteristic quantities inthe k-th rectangular wave chart are denoted SV1 k to SV5 k, two piecesof the test pattern information are denoted SHk/Smax and SLk/Smax, theobjective is achieved when SV1 k to SV5 k are inputted in terms of everyk and SHk/Smax and SLk/Smax are outputted.

[0311] If the intensity of the synapse coupling between each element atthe time of the completion of learning, and when the characteristicquantity of the rectangular wave chart is inputted to the network, thetest pattern information on the rectangular wave chart, which is theinformation on the highest signal value and lowest signal value in thisembodiment example, can be obtained. When this is realized, an outputresult based on actual learning is retrieved in the network and, evenwhen information on any image about which the network has not learntactually is inputted, an output exhibiting the tendency of the image canbe obtained. For example, as shown in FIG. 4(a) and FIG. 4(b), we haveactually confirmed that, in case of an image recorder in which thesharpness tends to decrease as the image signal value increases, anoutput result exhibiting the tendency is obtained.

[0312] The image feature {γ} can be a quantitative value calculated fromthe image signal value as described in this embodiment example, but notlimited thereto, and any quantified value of subjective evaluation canbe used instead. In addition, the characteristic quantity of an imageneeds not be limited to a function of the original image signal orunsharp image signal but can be any characteristic quantity including aphysical structure such as bone or organ of a subject, a shape beingsuch as oblong or circular, and size of the shape.

[0313]FIG. 8(c) shows a diagram where the processing 1 and processing 2are integrated into one network. The explanation above has been made onthe “sharpness compatibility” and “image structure check-up” separatelyfor the sake of convenience, but they can be processed in one time ifthe output element of the “image structure check-up” network and theinput elements of the “sharpness compatibility” network are connectedeach other to integrate the two networks. In this figure, the networkcomprises two-layer perceptron and a learning method based on the errorinverse-propagation is employed, where the image feature SV1 to SV5 areinputted to the input elements (Input1 to Input5) and the sharpnessinformation α is outputted from the output element (Output). It ispreferable to omit an intermediate computation on the “test patterninformation” and employ a network computation that correlates the “imagestructure set-up” and “sharpness information” as shown in the figure,because the computation steps can be reduced from two to one. As aresult, the integrated network itself turns to be nothing but a“sharpness compatibility” network.

[0314] The neural network in FIG. 8(a) to (c) may be a single layernetwork (input layer and output layer), but use of a multiple layernetwork (input layer, output layer, and one or more intermediate layers)is preferable to improve the calculation accuracy.

[0315] A suitable method for comparing {α} with {β}, {β} with {γ}, or{α} with {γ} is not limited to neural network but the least squaremethod is also applicable. For example, in comparing {α} to {γ}, αk isused as the sharpness information of the k-th test pattern and γk isused as the image feature extracted from the test pattern. For example,it can be a possible method for calculating γk that an equationγk=(a·{Sk(0)−Sk(1)}+b){c·Sk(M)+d} is employed and each a, b, c and d isdetermined so that the error-square-sum Σ(αk−γk){circumflex over ( )}2becomes smallest. An equation for calculating the image feature γk isnot limited to the above, and use of more complicated equation enablesto extract the image feature and compare it with the sharpnessinformation based on the chart image more appropriately.

[0316] The sharpness information {α} can be an index value itself suchas MTF, SWTF or ARTF obtained through physical evaluation, or, whenapproximating the spatial frequency characteristic using a specifiedfunction type, a parameter specific to the function type may be applied.Various ways can be thought out, including, for example, a case wherethe MTF characteristic is regarded as a Gauss function, in which itsvalue is 1 at the zero frequency and MTF attenuates as the spatialfrequency increases, and the standard deviation obtained through theleast square or the like is regarded the sharpness information {α}.Naturally, the approximation expression for MTF may be a polynomial orLorentz function, and the type of the function does not matter providedthat the MTF characteristic can be approximated precisely. Differentmethods other than the above can be applied also to the sharpnessadaptive calculation.

[0317] <Suitable Sharpness Correction (Retrieving Phase)>

[0318] The sharpness correction in the retrieving phase is explainedhereunder. FIG. 18(b) shows an outline of processing in the retrievingphase. In this phase, appropriate sharpness level correction can beperformed for an image signal that needs to be corrected of itssharpness level.

[0319] In this embodiment example, the image feature {γ} 312 isextracted (Computation (1) in FIG. 18(b)) from an image that needs to becorrected of its sharpness level; the image structures in the peripheryof each pixel are checked up (Computation (2) and Computation (3) inFIG. 18(b)) based on the image feature {γ} 312; and appropriatesharpness correction is performed (Computation (4) in FIG. 18(b)) in theimage processing means 110 based on the analogized sharpnesscharacteristic. Then, the result is outputted, as needed, in the form ofhard copy from the image recording means 121. With this processing,appropriate sharpness correction can be performed even on any imagesignal that does not form a test pattern like a rectangular wave chart.

[0320] Although the sharpness information and correction parameter areset as one same object in the present embodiment example, but it is ofno restriction. If the sharpness information and correction parameterare not the same one, it will be sufficient to provide another means forrelating them to each other. It is preferable, however, to set the twoas one same object as in this embodiment example, because computationcan be simplified and processing time can be reduced.

[0321]FIG. 9(a), FIG. 9(b), FIG. 9(c) and FIG. 9(d) show an effect ofthe sharpness level correction, taking an actual profile for example.The image recorder is assumed to have a tendency that, when thesharpness level is measured using a rectangular wave chart where theimage signal amplitude decreases gradually as in FIG. 9(a), thesharpness level decreases as the image signal amplitude decreases as inFIG. 9(b). FIG. 9(c) is an example of image signal value profile, andFIG. 9(d) shows the positional characteristic (profile) of thecorrection parameter α determined by the sharpness level comparing meansof the present embodiment example. When an image based on the imagesignal shown in FIG. 9(c) is recorded using the above image recorder,because α is restrained in areas with greater image signal amplitude andexaggerated in areas with smaller image signal amplitude, a hard copy onwhich deteriorated sharpness level is appropriately corrected at anyposition (area) can be obtained.

[0322] <Effect of Sharpness Correction>

[0323] The easiest way to verify the effect of the sharpness levelcorrection by the present embodiment example is to produce each image(hard copy), recorded on a recording medium by the image recorder, basedon the original image signal and on the processed image signal subjectedto the sharpness level correction of the original image signal, andcompare with each other and evaluate the effect by visual observation orphysical evaluation of the images. In this way, the correction effectcan be verified qualitatively.

[0324]FIG. 10(a), FIG. 10(b), FIG. 10(c) and FIG. 10(d) show an exampleof the relationship between the image profile (signal value profile) andthe correction parameter. FIG. 10(a) shows the signal value of anoriginal signal having 3 pixels per line and FIG. 10(b) shows the signalvalue of the image that has been corrected on 3 pixels per line by thepresent embodiment example.

[0325]FIG. 10(c) shows the signal value of an original signal having 6pixels per line and FIG. 10(d) shows the signal value of the image thathas been corrected on 6 pixels per line by the present embodimentexample.

[0326]FIG. 11 shows the result of functional evaluation through visualobservation of mammography. For the functional evaluation, breasts arephotographed on intensified paper CM-100/film New CM-H, the film isdeveloped on an automatic developing machine TCX-201, the photos aredigitized into image signals on a film digitizer LD-5500 (allmanufactured by Konika Co., Ltd.), and then the images for diagnosis arerecorded on a recording medium, using an ink-jet recorder (prototype).The film was processed for development under a condition of developmenttemperature 35° C. and drying temperature 32° C. for 90 seconds. Theexample of the prior art is an image that is recorded without beingsubjected to the sharpness level correction, and each embodiment example1 to 4 is an image that is recorded after being subjected to somesharpness level correction. The embodiment example 1 is an imagesubjected to the sharpness level correction processing using 2 unsharpmasks (simple mean mask of m=1 to 2, of which mask size is 3 and 5because the mask size of the m-th simple mean mask is (2 m+1)). Theembodiment example 2 is an image subjected to the sharpness levelcorrection processing using 3 unsharp masks (simple mean mask of m=1 to3, of which mask size is 3, 5 and 7). The embodiment example 3 is animage subjected to the sharpness level correction processing using 4unsharp masks (simple mean mask of m=1 to 4, of which mask size is 3, 5,7 and 9). The embodiment example 4 is an image subjected to thesharpness level correction processing using 5 unsharp masks (simple meanmask of m=1 to 5, of which mask size is 3, 5, 7, 9, and 11). Each imageis place on a film viewer with the illuminance of 10,000 lx. forsubjective evaluation through visual observation of the resolution ofmicro-calcification in the breasts. In the observation, the resolutionof the micro-calcification is evaluated into four grades of “⊚”, “∘”,“Δ” and “×” (wherein the judgment criterion is “⊚”: Extremely high, “∘”:High, “Δ”: Fairly low, and “X”: Not detectable because of blurredmicrostructure).

[0327] As a result of the functional evaluation, it is confirmed thatthe sharpness level correction can be performed with sufficient accuracyprovided that the number of unsharp masks is three or more. With theimage processing method according to the present embodiment example, itbecomes possible to appropriately correct the sharpness level of amicrostructure like micro-calcification having a size as small asseveral-tens micrometer without losing its shape.

[0328] Next, a more objective way for verifying the effect of thesharpness level correction is described hereunder. To check thevariation between the sharpness level characteristics before and afterthe image conversion step, it will be the easiest to visually observeand compare things in the same state as explained above. Comparison,however, is sometimes difficult because the image information containedin two objects to be compared with each other, such as subject to bephotographed and image signal or image signal and hard copy, hasdifferent state. Explained hereunder is an example of verifying theeffect of the sharpness correction in each image conversion step throughphysical evaluation of the image.

[0329] When the image conversion step is performed in an image recorder,generate an image signal (original image signal) representing therectangular wave chart, to start with, and obtain the processed imagesignal (corrected image signal) of which frequency has been exaggeratedtaking into account the deterioration of the sharpness level in theoriginal signal caused in the image recorder. And then, record therectangular wave chart on an recording medium based on the image signal,and measure a density profile of the rectangular wave chart, using amicro-densitometer, to see if the sharpness level is constant (in caseof MTF value, it is approximately 1 at the end spatial frequency).Although the result varies by method applied for the sharpness levelevaluation, it goes true that the sharpness levels before and after theimage conversion step become approximately equal if the sharpness levelin the density profile falls within a range of 0.85 to 1.15. That thesharpness levels before and after the image conversion step “becomeapproximately equal” means each of the images before and after the imageconversion step has the information of nearly equal quantity in eachspatial frequency band.

[0330] The range 0.85 to 1.15, in which it can be judged that thesharpness levels before and after the image conversion step isapproximately equal, has been determined through the functionalevaluation by visual observation of a hard copy recorded by theafore-mentioned image recorder, and deterioration of the sharpness levelcan hardly be recognized by visual observation so far the sharpnesslevel is within the above range. Since minute difference in thesharpness level can be detected satisfactorily by visual observationparticularly in a relatively low frequency band of 0 to 3.0 cycle/mm, itis preferable to perform the sharpness level correction so that thesharpness levels before and after the image conversion step becomeapproximately equal within a range of 0 to 3.0 cycle/mm.

[0331] Even in an image conversion step that is performed by other typeof device than image outputting device, being approximately equal or notcan be judged, in a similar manner as in the case using the imagerecorder, by checking whether the ratio between the sharpness levelsbefore and after the image conversion step is within a range of 0.85 to1.15. The ratio between the sharpness levels before and after the imageconversion step means (sharpness level of an output object)/(sharpnesslevel of an input object).

[0332] When the image conversion step is performed in an image display,generate an image signal (original image signal) representing therectangular wave chart, and obtain the processed image signal (imagesignal corrected with exaggerated sharpness level) of which sharpnesslevel has been exaggerated from the original image signal through someexaggeration processing. And then, display the rectangular wave chartimage on a display screen based on the image signal, photograph thedisplay screen with a photographing device such as CCD camera, andmeasure a luminance profile of the rectangular wave chart. Then, findthe sharpness level based on the luminance profile and see if thesharpness level is constant (in case of MTF value, it is approximately 1at the end spatial frequency).

[0333] When the image conversion step is performed in an image inputtingdevice, photograph a subject having definite contour, such as leadchart, slit or step wedge, to obtain the image signal representing thesubject, to start with, and obtain the processed image signal (correctedimage signal) of which frequency has been exaggerated taking intoaccount the deterioration of the sharpness level in the original signalcaused in the image inputting device. And then, measure the sharpnesslevel on the corrected image signal to see if the sharpness level isconstant (in case of MTF value, it is approximately 1 at the end spatialfrequency). Herein, because it is difficult to obtain an original imagesignal that is equivalent to the contour of the subject, the contour ofthe subject is analogized based on the gradation characteristic (forexample, X-ray irradiation quantity vs. signal value characteristic) ofthe image inputting device. That is to say, it is assumed that theoriginal image signal equivalent to a lead chart exhibits a rectangularwave which maintains its amplitude at any high spatial frequency.

[0334] When the image conversion step is performed in an imageprocessor, generate an image signal (original image signal) representingthe rectangular wave chart, to start with, and obtain the processedimage signal (image signal corrected with exaggerated sharpness level)of which sharpness level has been exaggerated from the original imagesignal through some exaggeration processing. And then, perform thesharpness level correction processing for correcting the sharpness levelso as to cancel the sharpness level exaggeration processing to obtainthe processed image signal (corrected image signal). Then, find thesharpness level of the corrected image signal to see if the sharpnesslevel is constant (in case of MTF value, it is approximately 1 at theend spatial frequency).

[0335] Besides, the present embodiment example is also applicable to theimage processing step or image displaying step. Since the output objectin the “image processing step” is an image signal, it is possible, forexample, to generate an image signal equivalent to the rectangular wavechart and evaluate the sharpness level by computation based on theprofile data of the processed image. Since the output object in the“image displaying step” is the illuminance or luminance near the displaysurface, it is possible, for example, to photograph the object by a CCDcamera and return it to an image signal to evaluate the sharpness.Evaluation is not limited to physical evaluation, but quantifying theresult of subjective evaluation through visual observation is alsoacceptable.

[0336] <Supplementary Explanation>

[0337] The “mask” in the above description is an object relating to therange or weight factor acting upon the peripheral pixels in an imagesignal, and defined as “an object that gives a (un-) sharpness effect interms of positional space”. On the other hand, the “filter” is an objectrepresenting an exaggerating and attenuating effect in each frequencyband, and defined with a nuance “an object that gives a (un-) sharpnesseffect in terms of spatial frequency”. Conventionally, however, theyhave been frequently used in mixture, and so “mask” and “filter” areregarded as same one in the present embodiment.

[0338] ARTF, referred in the above description, means the sharpnesslevel evaluation index about which the inventor of the present inventionhas applied for patent under Japanese Patent Application No.2001-224493. FIG. 12 shows a flowchart for analyzing ARTF, wherein thespatial frequencies of a sine wave acting upon the profile are selectedby small increment, with the calculation start point fixed, andsubjected to discrete Fourier transformation, and a desired spatialfrequency is detected based on the calculation result.

[0339] S1 in FIG. 12 reads the chart information. The information mayinclude, for example, density profile of the chart image, re-samplinginterval of the densitometer, number of the chart elements, designspatial frequency of each chart element, number of lines of the chartelement, target calculation start point, type of the chart to be used,and any other supplementary information on the chart that is necessaryto speed up the analysis.

[0340] S2 of FIG. 12 converts the physical quantity as needed. Densityprofile is mostly used as the physical quantity for image evaluation inthe medical field but, not limited thereto, transmittance or contrastprofile can be used instead as needed by application.

[0341] S3 of FIG. 12 sets the calculation start point and calculationrange. The calculation start point must be selected at one point on theprofile having continuous cyclic waveform, and improper setting resultsin serious error in the calculation result. When the shape of the chartimage profile is already known, the target calculation start point thathas been read into in S1 (to be concrete, a plot number corresponding toeach chart element) can be used. However, since the density measurementstart point is indefinite, it is desirous to set the point in accordancewith each profile. When setting the calculation range, it is importantto meet the spatial frequency of the output chart image with that of theactive sine wave. To reduce error in the calculation, calculationdistance must be set to the cycle of the output image or to an integralmultiple of the cycle.

[0342] The calculation start point must be selected at one point on theprofile having continuous cyclic waveform, and improper setting resultsin serious error in the calculation result.

[0343]FIG. 13(a) is an explanatory figure showing an example of settingthe calculation start point for Fourier analysis in each chart element.For the sake of ease, FIG. 13(a) takes for example a chart image densityprofile with three (N=2) chart elements. To start with, find the densitypeak, high level DH(Uk) and low level DL(Uk) in each spatial frequencyUk. Then, select D* with which DH(Uk)<D*<DL(Dk) holds true for any i. InFIG. 13(a), for example, any D* is acceptable provided thatDmin(U2)<D*<Dmax(U2) holds true. Any marked (∘) portion on the firsttransition of each peak can be selected as the calculation startpoint=P0(Uk). To select each po(Uk), the boundary on the charted portionof each spatial frequency must be recognized. When, for example, theevaluation is always done using a chart image of similar shape, andprovided that a target calculation start point is stored beforehand oran inputting means is provided, making the best of a fact that theprofile shape is always nearly equal, the calculation start point can beeasily set based on the given information. Besides, this information canbe replaced by other chart information such as the number of the chartelements and each spatial frequency, number of peaks of each chartelement, and distance between the chart elements.

[0344]FIG. 13(b) shows the range for calculating the amp of the chartimage profile. Because the peak profile may be different on a peak ateach end and it can result in different calculation result, it ispreferable not to use all peaks for the calculation but to analyzemultiple consecutive peaks near the center. Ten peaks are shown in thefigure but using the central seven cycles of peaks is preferable for thecalculation.

[0345] S4 of FIG. 12 judges whether the calculation of amp has completedfor all chart elements. When a chart comprising (N+1)-number of chartelements of different spatial frequencies (U0, U1, - - - , UN) isemployed, the processing is repeated (S4 of FIG. 12 to S6 of FIG. 12)until the calculation of all amp(Uk) (k=0, 1, . . . , N) are complete.Upon completion of the calculation, normalize amp(Uk), using anormalization spatial frequency, to obtain ARTF (amplitude-rate transferfunction) (S7 of FIG. 12). ARTF is calculated in an equation:

ARTF(Uk)=amp(Uk)/amp(U0)

[0346] As a result of finding the rectangular wave chart through directFourier transformation using the above ARTF, it becomes possible toperform more strict sharpness level correction corresponding to thefrequency characteristic.

[0347] Besides, it is likely to carry out periodic maintenance of eachdevice so as to obtain favorable image quality all the time. If, forexample, a chart image is stored in the image recorder and a means forinputting the measurement result of the image is provided, tuning theimage quality becomes possible. By repeating the steps of generation ofimage→printing→evaluation→result→ . . . , favorable image qualitybecomes available at any time.

[0348] When decoding Snew: processed image signal, Sm: original imagesignal (m=0 in case of the original image signal) or unsharp imagesignal generated through the m-th unsharp masking processing (m=1 to Min case of the unsharp image signal), Cm: weight factor in the originalimage signal (m=0 in case of the original image signal) or weight factorin the m-th unsharpe image signal (m=1 to M in case of the unsharp imagesignal), it is preferable that the afore-mentioned frequency processingis performed based on a computation formula:

Snew=ΣCmSm

[0349] Furthermore, in case of a two-dimensionally configured imagesignal, where the sharpness level characteristic, for example, in themain scan direction differs significantly from that in the sub scandirection (in other words, anisotropic by the scan direction), atwo-dimensional unsharp mask that meets the characteristic cansatisfactorily be employed. Otherwise, to reduce the processing time, itis acceptable to find a mean of the sharpness characteristics in themain scan direction and sub scan direction and perform the sharpnesscorrection based on the mean.

[0350] Besides, in this embodiment example, the unsharp mask can be atwo-dimensional Gauss function type filter or Laplacian filter, notlimited to a simple filter of Nh×Nv (Nh: vertical mask range, Nv:horizontal mask range), and any combination of similar filters withdifferent resolution is applicable to the embodiment example. Themasking processing using a binomial filter obtained through repeatedprocessing with a 2×2 simple mean filter is explained hereunder.

[0351]FIG. 14 shows a 2×2 simple mean filter used in this embodimentexample. It is understood from FIG. 14 that the weight is ¼ at anysection and therefore this is a simple mean filter. As a result ofrepeated filtering with this simple mean filter, the weight becomes nolonger a simple mean.

[0352]FIG. 15 shows the relationship (in a state with no normalization)between the distribution in 8-cycle count repeated processing with asimple mean filter and the Gaussian distribution. The upper line showsthe filtering weight in the repeated processing with a simple meanfilter for 8 cycles, and the lower line shows the Gaussian distributionwith the dispersion factor=2. By comparing these two, it is understoodthat the weight factor resembles to the Gaussian distribution.

[0353] In this embodiment example, by repeated filtering with a simplemean filter as shown in FIG. 14, filtering processing can be performedfaster than when a conventional weighting filter is employed, andhigh-speed processing becomes possible even for an algorism, such as apyramid algorism, that requires repeated filtering processing. Inaddition, while, in a prior art, the mask must be exchanged when thefrequency characteristic needs to be changed, the frequencycharacteristic can be changed simply by changing the number of filteringprocessing with a simple mean filter, without changing the mask, in thepresent embodiment example. In other words, the frequency characteristiccan be controlled by specifying the number of repeated simple-meanprocessing.

[0354] The mask to be applied shall be a 2×2 simple mean filter as shownin FIG. 14, and unsharp image signal can be generated through repeatedfiltering with this filter. This mask has a characteristic that theresult of repeated filtering with this mask comes closer to that of aGaussian mask. For example, two cycles of masking processing with thismask is equal to the masking processing with a weighted filter shown inFIG. 16(b).

[0355] Repeating the filtering processing with this mask for more cyclescauses higher equalization and, therefore, the frequency characteristicof the unsharp image signal exhibits a profile where high frequenciesare dropped off. FIG. 17 shows the cycle count of the filteringprocessing and the response. Horizontal axis is the frequency f andvertical axis is the response. A characteristic as shown by f1 in FIG.17 is exhibited when the cycle count of the repeated filtering is smallbut a characteristic as shown by f2 in FIG. 17 is exhibited when thecycle count of the repeated filtering is big. When the cycle count ofthe repeated filtering is big, high-frequency component signals aredropped off and the frequency characteristic is lower.

[0356] <Second Embodiment Example: Method of Decomposing Original ImageSignal into Each Frequency Band>

[0357] In this second embodiment example, a multi-resolution method fordecomposing an original image signal into each frequency band isapplied. It is a method, where multiple unsharp image signals havingdifferent sharpness level, that is, different frequency responsecharacteristics from an original image signal are generated, andmultiple limited-band image signals (hereinafter called band passsignals) that represent the frequency components of limited frequencybands of the original image signal are generated by calculating thedifferential between two signal values out of the unsharp image signalsand original image signal. Then, the band pass signals are restricted toa desired size using each specific function, and an add signal isgenerated by integrating the multiple restricted band pass signals.

[0358] To start with, explanation below is made on the pyramid algorismbased on which the present embodiment example is applicable. FIG. 19 isa block diagram showing an example construction of the decomposingsection that performs the pyramid algorism. In FIG. 19, symbol ↑ showsinterpolation, ↓ shows down sampling, and F shows filtering.

[0359] In the second embodiment example, the conversion processing, tobe described later, is performed on the unsharp image signal ordifferential image signal obtained from the pyramid algorism. Pyramidalgorism is an algorism where an image signal with the resolutioncorresponding to the frequency component signal is generated andprocessed by down-sampling an image. For this reason, in this invention,having different resolution means that the image obtained by the pyramidalgorism has different resolution.

[0360] As shown in FIG. 19, when a digital image signal S representingan original signal is inputted into a multi-resolution decompositionprocessing means 30, it is filtered with a low pass filter in afiltering means 20. The original image signal S, subjected to filteringwith this filter, is sampled (down sampling) by every other pixel in thefiltering means 20, and a low-resolution approximate image signal g1 isobtained.

[0361] The low-resolution approximate image signal g1 has a ¼ size ofthe original image signal S. Then, in an interpolating means 21,interpolation processing is performed into the sampled space of thelow-resolution approximate image signal g1. The interpolation processingis, for example, to insert a row and a column comprising 0 into everyother row and every other column of the low-resolution approximate imagesignal g1, respectively.

[0362] The low-resolution approximate image signal g1 interpolated withpixels comprising 0 as above is a blurred image, but, because a pixelcomprising 0 is inserted between every other pixel, change of the signalvalue is no longer smooth.

[0363] Then, after the interpolation as above, the low-resolutionapproximate image signal g1 is again subjected to the filteringprocessing with the low pass filter and another low-resolutionapproximate image signal g1′ is obtained. When compared with the abovelow-resolution approximate image signal g1 subjected to theinterpolation processing, change of the signal value of thislow-resolution approximate image signal g1′ is smoother.

[0364] Instead of filtering with a low pass filter after interpolationprocessing with 0 is performed as above, it is acceptable that aninterpolation processing is first performed on the columns by linearinterpolation, spline interpolation, or weighting in accordance with thesampling function and then a similar processing is performed on therows.

[0365] When compared with the original image signal in terms offrequency band, the obtained image signal is such that frequencieshigher than a half are lost.

[0366] Then, in a subtracter 22, the low-resolution approximate imagesignal g1′ is subtracted from the original image signal S, and adifferential image signal b0 is obtained.

[0367] This subtraction is performed between the signals of the pixelsof the original image signal S and low-resolution approximate imagesignal g1′ corresponding to each other. Because, as explained above, theimage of the low-resolution approximate image signal g1′ seems blurredin the frequency bands higher than a half of the spatial frequency ofthe original image signal, the differential image signal b0 turns to bean image signal that represents only the frequency bands higher than ahalf of the original image signal.

[0368] Next, the low-resolution approximate image signal g1 is inputtedinto the filtering means 20 and subjected to the filtering processingwith the low pass filter.

[0369] Then, the low-resolution approximate image signal g1, subjectedto filtering, is sampled by every other pixel in the filtering means 20,and a low-resolution approximate image signal g2 is obtained. Thelow-resolution approximate image signal g2 has a ¼ size of thelow-resolution approximate image signal g1, that is, a {fraction (1/16)}size of the original image signal S.

[0370] Then, in the interpolating means 21, interpolation processing isperformed into the sampled space of the low-resolution approximate imagesignal g2.

[0371] The interpolation processing is, for example, to insert a row anda column comprising 0 into every other row and every other column of thelow-resolution approximate image signal g2, respectively. Thelow-resolution approximate image signal g2 interpolated with pixelscomprising 0 as above is a blurred image, but, because a pixelcomprising 0 is inserted between every other pixel, change of the signalvalue is no longer smooth.

[0372] Then, after the interpolation as above, the low-resolutionapproximate image signal g2 is again subjected to the filteringprocessing with the low pass filter and another low-resolutionapproximate image signal g2′ is obtained. When compared with the abovelow-resolution approximate image signal g2 subjected to theinterpolation processing, change of the signal value of thislow-resolution approximate image signal g2′ is smoother.

[0373] Instead of filtering with a low pass filter after interpolationprocessing with 0 is performed as above, it is acceptable that aninterpolation processing is first performed on the columns by linearinterpolation, spline interpolation, or weighting in accordance with thesampling function and then a similar processing is performed on therows.

[0374] When compared with the low-resolution approximate image signal g1in terms of frequency band, the obtained image signal is such thatfrequencies higher than a half are lost.

[0375] Then, in the subtracter 22, the low-resolution approximate imagesignal g2′ is subtracted from the low-resolution approximate imagesignal g1, and a differential image signal b1 is obtained.

[0376] This subtraction is performed between the signals of the pixelsof the low-resolution approximate image signal g1 and low-resolutionapproximate image signal g2′ corresponding to each other. Because, asexplained above, the image of the low-resolution approximate imagesignal g2′ seems blurred in the frequency bands higher than a half ofthe spatial frequency of the low-resolution approximate image signal g1,the differential image signal b1 turns to be an image signal thatrepresents only the frequency bands higher than a half of thelow-resolution approximate image signal g1.

[0377] In other words, as shown in FIG. 20, the differential imagesignal b1 is an image signal that represents only the frequency bandshigher than a half of the low-resolution approximate image signal g1,that is, only the frequency bands from N/4 to N/2 of the Nyquistfrequency N of the original image signal. Although the differentialimage signal is obtained through the filtering processing with a lowpass filter, the result is practically the same as obtained through thefiltering processing with a Laplacian filter because the filtered imagesignal is subtracted from the low-resolution approximate image signal.

[0378] Then, the above processing is repeated on the filtered andsampled low-resolution approximate image signal gk (K=0 to L-1) oneafter another to obtain L-number of differential image signal bk (k=0 toL-1) and residual image signal gL of the low-resolution approximateimage signal. The differential image signal bk exhibits lower resolutionin sequence starting from b0. This is because the frequency band of theimage signal becomes lower, and, when compared with the Nyquistfrequency of the original image signal, the differential image signal bkhas the frequency band of N/2{circumflex over ( )}(k+1) toN/2{circumflex over ( )}k and the image signal size is ½{circumflex over( )}(2k) times the original image signal.

[0379] In other words, the size of the differential image signal b0 thatexhibits the highest resolution is the same as the original image signalbut that of the differential image signal b1 that exhibits the nexthighest to the differential image signal b0 is ¼ of the original imagesignal.

[0380] Because the differential image signal size becomes smaller insequence starting from the same size as of the original image signal andthe differential image signal is practically the same image signal asobtained through the processing with a Laplacian filter, themulti-resolution conversion in the second embodiment example issometimes called the Laplacian pyramid algorism.

[0381] The residual image signal gL can be regarded as an approximateimage signal of the original image signal with very low resolution and,in an extreme case, the residual image signal gL may comprise a singleimage signal representing a mean of the original image signal.

[0382] Herein, the residual image signal gL, which is equivalent to thelowest frequency image signal, means an image signal that is obtained asa result of the L-th filtering processing when the pyramid algorism isperformed on the original image signal and the filtering processing isrepeated 1 to L-number of times.

[0383] Then, the differential image signal bk obtained as above isstored in a memory, not known. And then, the image conversionprocessing, to be described later, is performed on g1′, g2′, g3′, . . ., which is the output from the interpolating means 21 shown in FIG. 19,or on b0, b1, b2, . . . . These unsharp image signals g1′, g2′, g3′, areunsharp signals of multiple frequency bands having different frequencycharacteristic.

[0384] The differential image signal generated from the unsharp imagesignal subjected to the conversion processing or the differential imagesignal subjected to the conversion processing are now convertedinversely. This inverse conversion processing is performed in a recoveryprocessing means 40.

[0385]FIG. 21 is a block diagram showing an example construction of therecovery section that performs the pyramid algorism. The image signalsto be processed in this example are b0 to bL-1. First, the image signalbL-1 is interpolated by insertion between each pixel by theinterpolating means 24 and turns to an image signal b1-1′ with 4 timesthe size of the previous size. Then, in an adder 25, the interpolatedimage signal bL-1′ is added to the lowest resolution differential imagesignal bL-2 between the pixels corresponding to each other, and theadded image signal (bL-1′+bL-2) is obtained.

[0386] Next, the added image signal (bL-1′+bL-2) is inputted into theinterpolating means 24, and then interpolated by insertion between eachpixel by the interpolating means 24 and turns to an image signal bL-2′with 4 times the size of the previous size. Then, in the adder 25, theimage signal bL-2′ is added to the differential image signal bL-3, whichhas higher resolution than the differential image signal bL-2 by onerank, between the pixels corresponding to each other. And then, theadded image signal (bL-2′+bL-3) is interpolated by insertion betweeneach pixel by the interpolating means 24 and turns to an image signalbL-3′ with 4 times the size of the differential image signal bL-3.

[0387] The same processing is repeated thereafter. This processing isperformed sequentially on higher frequency differential image signaland, finally, the differential image signal b0′ interpolated in theadder 25 is multiplied by β in a multiplier 26 and then added to theoriginal image signal S in an adder 29 to obtain the processed imagesignal Sout (frequency exaggeration processing).

[0388] As explained in the preferred embodiment, it is possible torealize an image processing method and an image converting apparatusthat can provide image in stable quality irrespective of the differencein the density of image to be recorded or difference in the conversioncharacteristic of each device.

[0389] Disclosed embodiment can be varied by a skilled person withoutdeparting from the spirit and scope of the invention.

What is claimed is:
 1. A method for processing original image signals ofan image composed of a plurality of pixels, said method comprising thesteps of: converting said image in respect to said original imagesignals; extracting image features for every pixel included in saidplurality of pixels in respect to said original image signals; andperforming a frequency processing operation for compensating for asharpness level of said image, based on said image features extracted insaid extracting step, so as to reduce a variation of said sharpnesslevel before and after said converting step.
 2. The method of claim 1,wherein said converting step is performed after applying an imageinterpolation processing to said original image signals; and wherein,when an interpolating-magnification factor of said image interpolationprocessing is equal to or greater than a predetermined value, said imageinterpolation processing is performed after performing said frequencyprocessing operation, while, when said interpolating-magnificationfactor of said image interpolation processing is smaller than saidpredetermined value, said frequency processing operation is performedafter performing said image interpolation processing.
 3. The method ofclaim 1, wherein said image is a medical image.
 4. A method forprocessing original image signals of an image composed of a plurality ofpixels, said method comprising the steps of: converting said image inrespect to said original image signals; obtaining sharpness informationfrom an image converting apparatus by which said converting step isperformed; and performing a frequency processing operation forcompensating for a sharpness level of said image, based on saidsharpness information obtained in said obtaining step, so as to reduce avariation of said sharpness level before and after said converting step.5. The method of claim 4, wherein said converting step is performedafter applying an image interpolation processing to said original imagesignals; and wherein, when an interpolating-magnification factor of saidimage interpolation processing is equal to or greater than apredetermined value, said image interpolation processing is performedafter performing said frequency processing operation, while, when saidinterpolating-magnification factor of said image interpolationprocessing is smaller than said predetermined value, said frequencyprocessing operation is performed after performing said imageinterpolation processing.
 6. The method of claim 4, wherein said imageis a medical image.
 7. The method of claim 4, wherein said sharpnessinformation relate to at least one of SWTF, MTF and ARTF.
 8. A methodfor processing original image signals of an image composed of aplurality of pixels, said method comprising the steps of: convertingsaid image in respect to said original image signals; extracting imagefeatures for every pixel included in said plurality of pixels in respectto said original image signals; obtaining sharpness information from animage converting apparatus by which said converting step is performed;calculating correlation between said image features and said sharpnessinformation; and performing a frequency processing operation forcompensating for a sharpness level of said image, based on saidcorrelation calculated in said calculating step, so as to reduce avariation of said sharpness level before and after said converting step.9. The method of claim 8, wherein said converting step is performedafter applying an image interpolation processing to said original imagesignals; and wherein, when an interpolating-magnification factor of saidimage interpolation processing is equal to or greater than apredetermined value, said image interpolation processing is performedafter performing said frequency processing operation, while, when saidinterpolating-magnification factor of said image interpolationprocessing is smaller than said predetermined value, said frequencyprocessing operation is performed after performing said imageinterpolation processing.
 10. The method of claim 8, wherein said imageis a medical image.
 11. The method of claim 8, wherein a neural networkis employed for said calculating step.
 12. The method of claim 8,wherein said frequency processing operation is performed, based on aformula of, Snew=ΣCmSm  where m=0, . . . , M. M: natural number, Snew:processed image signal; Sm: original image signal (when m=0) or unsharpimage signal generated through a m-th unsharp masking processing (whenm=1 to M), Cm: weight factor in the original image signal (when m=0) orweight factor in the m-th unsharpe image signal (when m=1 to M)). 13.The method of claim 12, wherein said weight factor is determined, basedon said image features or said sharpness information.
 14. The method ofclaim 12, wherein said weight factor is determined so as to keep ΣCm ata constant value.
 15. The method of claim 12, wherein said frequencyprocessing is applied to image signals generated from a mammographicimage by employing at least three unsharp masks, modulation transferfunctions of which are different each other.
 16. A method for processingan image composed of a plurality of pixels, said method comprising thesteps of: applying an unsharp mask processing to original image signalsof said image, composed of said plurality of pixels, to create aplurality of unsharp image signals; integrating at least two of firstdifferential image signals between said original image signals and saidplurality of unsharp image signals, second differential image signalsbetween said unsharp image signals being different relative to eachother, third differential image signals between said original imagesignals and image signals obtained by integrating said firstdifferential image signals, fourth differential image signals betweensaid original image signals and image signals obtained by integratingsaid second differential image signals, fifth differential image signalsbetween lowest-frequency image signals for said original image signalsand image signals obtained by integrating said first differential imagesignals, and sixth differential image signals between saidlowest-frequency image signals for said original image signals and imagesignals obtained by integrating said second differential image signals,in order to generate a compensation signal; adding said compensationsignal to said original image signals or said lowest-frequency imagesignals for said original image signals to generate processed imagesignals; and performing a frequency processing operation forcompensating for a sharpness level of said image, by changing amodulation transfer function with respect to said unsharp maskprocessing, so as to reduce a variation of said sharpness level beforeand after a step of converting said image in respect to said originalimage signals.
 17. The method of claim 16, wherein said converting stepis performed after applying an image interpolation processing to saidoriginal image signals; and wherein, when an interpolating-magnificationfactor of said image interpolation processing is equal to or greaterthan a predetermined value, said image interpolation processing isperformed after performing said frequency processing operation, while,when said interpolating-magnification factor of said image interpolationprocessing is smaller than said predetermined value, said frequencyprocessing operation is performed after performing said imageinterpolation processing.
 18. The method of claim 16, wherein said imageis a medical image.
 19. The method of claim 16, wherein said frequencyprocessing is applied to image signals generated from a mammographicimage by employing at least three unsharp masks, modulation transferfunctions of which are different each other.
 20. The method of claim 16,wherein said unsharp mask processing is repeated processing with aspecific mask.
 21. The method of claim 20, wherein said specific mask isa simple mean mask.
 22. The method of claim 20, wherein said specificmask is a simple mean mask of 2-pixels by 2-pixels.
 23. The method ofclaim 1, wherein said frequency processing operation for compensatingfor said sharpness level of said image is performed so that frequencycharacteristics before and after said converting step coincides eachother in a predetermined frequency range.
 24. The method of claim 23,wherein said predetermined frequency range is 0-3.0 cycle/mm.
 25. Anapparatus for processing original image signals of an image composed ofa plurality of pixels, said apparatus comprising: an image convertingsection to convert said image in respect to said original image signals;an image feature extracting section to extract image features for everypixel included in said plurality of pixels in respect to said originalimage signals; and a frequency processing section to compensate for asharpness level of said image, based on said image features extracted byimage feature extracting section, so as to reduce a variation of saidsharpness level before and after said image converting section convertssaid image in respect to said original image signals.
 26. The apparatusof claim 25, wherein said image converting section converts said imagein respect to said original image signals after applying an imageinterpolation processing to said original image signals; and wherein,when an interpolating-magnification factor of said image interpolationprocessing is equal to or greater than a predetermined value, said imageinterpolation processing is performed after performing said frequencyprocessing operation, while, when said interpolating-magnificationfactor of said image interpolation processing is smaller than saidpredetermined value, said frequency processing operation is performedafter performing said image interpolation processing.
 27. The apparatusof claim 25, wherein said image is a medical image.
 28. An apparatus forprocessing original image signals of an image composed of a plurality ofpixels, said apparatus comprising: an image converting section toconvert said image in respect to said original image signals; asharpness information obtaining section to obtain sharpness informationfrom said image converting section; and a frequency processing sectionto compensate for a sharpness level of said image, based on saidsharpness information obtained by said sharpness information obtainingsection, so as to reduce a variation of said sharpness level before andafter said image converting section converts said image in respect tosaid original image signals.
 29. The apparatus of claim 28, wherein saidimage converting section converts said image in respect to said originalimage signals after applying an image interpolation processing to saidoriginal image signals; and wherein, when an interpolating-magnificationfactor of said image interpolation processing is equal to or greaterthan a predetermined value, said image interpolation processing isperformed after performing said frequency processing operation, while,when said interpolating-magnification factor of said image interpolationprocessing is smaller than said predetermined value, said frequencyprocessing operation is performed after performing said imageinterpolation processing.
 30. The apparatus of claim 28, wherein saidimage is a medical image.
 31. The apparatus of claim 28, wherein saidsharpness information relate to at least one of SWTF, MTF and ARTF. 32.An apparatus for processing original image signals of an image composedof a plurality of pixels, said apparatus comprising: an image convertingsection to convert said image in respect to said original image signals;an image feature extracting section to extract image features for everypixel included in said plurality of pixels in respect to said originalimage signals; a sharpness information obtaining section to obtainsharpness information from said image converting section; a sharpnesscalculating section to calculate correlation between said image featuresand said sharpness information; and a frequency processing section tocompensate for a sharpness level of said image, based on saidcorrelation calculated by said sharpness calculating section, so as toreduce a variation of said sharpness level before and after said imageconverting section converts said image in respect to said original imagesignals.
 33. The apparatus of claim 32, wherein said image convertingsection converts said image in respect to said original image signalsafter applying an image interpolation processing to said original imagesignals; and wherein, when an interpolating-magnification factor of saidimage interpolation processing is equal to or greater than apredetermined value, said image interpolation processing is performedafter performing said frequency processing operation, while, when saidinterpolating-magnification factor of said image interpolationprocessing is smaller than said predetermined value, said frequencyprocessing operation is performed after performing said imageinterpolation processing.
 34. The apparatus of claim 32, wherein saidimage is a medical image.
 35. The apparatus of claim 32, wherein aneural network is employed for said sharpness calculating section. 36.The apparatus of claim 32, wherein said frequency processing sectioncompensates for said sharpness level of said image, based on a formulaof, Snew=ΣCmSm  where m=0, . . . , M. M: natural number, Snew: processedimage signal; Sm: original image signal (when m=0) or unsharp imagesignal generated through a m-th unsharp masking processing (when m=1 toM), Cm: weight factor in the original image signal (when m=0) or weightfactor in the m-th unsharpe image signal (when m=1 to M)).
 37. Theapparatus of claim 36, wherein said weight factor is determined, basedon said image features or said sharpness information.
 38. The apparatusof claim 36, wherein said weight factor is determined so as to keep ΣCmat a constant value.
 39. The apparatus of claim 36, wherein saidfrequency processing section applies frequency processing to imagesignals generated from a mammographic image by employing at least threeunsharp masks, modulation transfer functions of which are different eachother.
 40. A apparatus for processing an image composed of a pluralityof pixels, said apparatus comprising: an image converting section toconvert said image in respect to said original image signals; an unsharpmask processing section to apply an unsharp mask processing to originalimage signals of said image, composed of said plurality of pixels, tocreate a plurality of unsharp image signals; an integrating section tointegrate at least two of first differential image signals between saidoriginal image signals and said plurality of unsharp image signals,second differential image signals between said unsharp image signalsbeing different relative to each other, third differential image signalsbetween said original image signals and image signals obtained byintegrating said first differential image signals, fourth differentialimage signals between said original image signals and image signalsobtained by integrating said second differential image signals, fifthdifferential image signals between lowest-frequency image signals forsaid original image signals and image signals obtained by integratingsaid first differential image signals, and sixth differential imagesignals between said lowest-frequency image signals for said originalimage signals and image signals obtained by integrating said seconddifferential image signals, in order to generate a compensation signal;an adding section to add said compensation signal to said original imagesignals or said lowest-frequency image signals for said original imagesignals to generate processed image signals; and a frequency processingsection to compensate for a sharpness level of said image, by changing amodulation transfer function with respect to said unsharp maskprocessing, so as to reduce a variation of said sharpness level beforeand after said image converting section converts said image in respectto said original image signals.
 41. The apparatus of claim 40, whereinsaid image converting section converts said image in respect to saidoriginal image signals after applying an image interpolation processingto said original image signals; and wherein, when aninterpolating-magnification factor of said image interpolationprocessing is equal to or greater than a predetermined value, said imageinterpolation processing is performed after performing said frequencyprocessing operation, while, when said interpolating-magnificationfactor of said image interpolation processing is smaller than saidpredetermined value, said frequency processing operation is performedafter performing said image interpolation processing.
 42. The apparatusof claim 40, wherein said image is a medical image.
 43. The apparatus ofclaim 40, wherein said frequency processing section applies frequencyprocessing to image signals generated from a mammographic image byemploying at least three unsharp masks, modulation transfer functions ofwhich are different each other.
 44. The apparatus of claim 40, whereinsaid unsharp mask processing is repeated processing with a specificmask.
 45. The apparatus of claim 44, wherein said specific mask is asimple mean mask.
 46. The apparatus of claim 44, wherein said specificmask is a simple mean mask of 2-pixels by 2-pixels.
 47. The apparatus ofclaim 25, wherein said frequency processing section compensates for saidsharpness level of said image, so that frequency characteristics, beforeand after said image converting section converts said image in respectto said original image signals, coincides each other in a predeterminedfrequency range.
 48. The apparatus of claim 47, wherein saidpredetermined frequency range is 0-3.0 cycle/mm.